The Lester and Sally Entin Faculty of Humanities
Department of Linguistics
THE ROLE OF SIMILARITY IN CO-OCCURRENCE
RESTRICTIONS: EVIDENCE FROM THE HEBREW
VERBAL SYSTEM
M.A. thesis submitted by
Hadas Yeverechyahu
Prepared under the guidance of:
Prof. Outi Bat-El
Dr. Evan-Gary Cohen
December 2014
ACKNOWLEDGEMENTS
Looking back on the five years I spent at the Linguistics department, as a BA and a
graduate student, I feel grateful for the opportunity I have been given to study and
explore the field I am passionate about the most. Many people have guided and
accompanied me on this fascinating way, and I would like to thank them for that.
First and Foremost, I would like to express my deep gratitude to my advisors,
Outi Bat-El and Evan-Gary Cohen. It was in Outi's and Evan's courses during my BA
studies that I discovered that my passion has a name - phonology. I would like to thank
them for teaching me, guiding me, and always encouraging me to ask the right questions
and seek for explanations open-mindedly. I appreciate their being always available for
consultation, and their extremely helpful comments, from insights about the analysis to
help in translating animals' names. I could not ask for better advisors.
I was fortunate to be a student of Lior Laks in my first semester at the
department, and ever since I benefited from his helpful advice and comments in
personal correspondence, and at the TAU colloquium. I would also like to thank Orna
Peleg, for her helpful comments on my research proposal, and for always showing
interest in my study.
I would like to thank other faculty members that I had the privilege to participate
in their courses: Galit Adam, Mira Ariel, Irena Botwinik, Julie Fadlon, Julia Horbath,
Aya Meltzer-Asscher, Nirit Kadmon, and Tali Siloni. I would also like to thank Moshe
Florentin of the Hebrew Culture department, who taught me everything I know about
the phonology and morphology of Biblical Hebrew.
The lexical analysis was completed with the help of Shmuel Bolozky who sent
me a digital copy of Evan-Shoshan dictionary. I would also like to thank the participants
i
in my experiments, and to Aya Vituri for the invaluable help with the statistical analysis
and insights.
I had the opportunity to present my study in several forums, and I would like to
thank the audiences of the TAU colloquium, the Israeli Phonology Circle, and IGDAL
2 for their interesting comments.
I would like to thank Tal Oded and Ruti Zusman, the former and current
secretaries of the department (respectively), whose doors were always open. Thank you
for all the help (and always in no-time) and for always opening my day with a smile.
The department was my second home in the last five years, and I had the honor
to share it with other excellent young linguists. I would like to thank my phonologist
friends, Daniel Asherov, Si Berrebi, Yael Firer, Noa Karni, Enav Kedar, Stav Klein,
Ezer Rasin, and Hadass Zaidenberg, for their helpful insights along the way. I would
also like to thank Noa Brandel, Hila Davidovich, Noa Geller, Dolly Goldenberg, Omer
Goldman, Anat Grubin, Netanel Haim, Ela Hillel, and Shiran Ofek for their comments
on my experiments and for helping distributing them. Finally, special thanks to my dear
friend, Tali Arad, for her wise comments, her advice, ideas and support. And to all of
you, I would like to thank for the friendship and support along the way.
I would also like to thank my non-linguistic friends, and especially to Naomi
and Ohad Feldheim (and their adorable son Lahav), for hours of delightful talks about
linguistics in general and on my thesis in particular, and for their interesting insights.
Last but not least, I would like to thank my parents and my brother, for their
great support and interest in my research. And finally, I would like to thank Chen Gafni.
Thank you for always being there for me both professionally and personally, for your
comments about any idea and thought, for reading every draft, and above all, thank you
for your endless love and support.
ii
ABSTRACT
In Semitic languages, homorganic consonants tend not to co-occur within the same stem
(Greenberg 1950). Previous studies (e.g. McCarthy 1981, 1986; Frisch et al. 2004)
suggested that these restrictions are due to similarity effects, that is, the greater the
similarity between two (homorganic) consonants, the less likely they are to co-occur.
The current study examines the restrictions in the Hebrew verbal system. I ask how
similarity between consonants contributes to restrictions, and whether they are due to a
universal constraint or influenced by language-specific factors.
The study has three main parts. First, I applied Frisch et al.'s (2004) similarity
model to the Hebrew consonant inventory. Second, I analyzed the Hebrew verbal
lexicon, focusing on the co-occurrences of C1-C2 stem consonants in the verb classes
kal (CaCaC) and pi'el (CiCeC). The analysis shows a highly significant correlation
between the similarity scale and the lexicon, and also suggests that place of articulation
has a major role in the restrictions (compared to other features). To strengthen and
complement the lexical analysis, I conducted two psycholinguistic experiments: a
lexical decision task and a word-likelihood judgment task, both examine the cooccurrence restrictions in the speakers' phonological system. The results of the
judgment task were highly correlated with the similarity scale and with the lexical
analysis. The experiments also highlight the role of place features in the restrictions.
These findings suggest that there are similarity based co-occurrence restrictions
on stem consonants C1-C2, both in the lexicon and in the speakers' phonological system.
They also suggest that place features have a major role in the restrictions, such that
consonants that share the major place feature are less likely to co-occur. However, the
experiments cannot suggest whether the influence of similarity on the grammatical
system is direct, or indirect through the lexical influences.
iii
TABLE OF CONTENTS
1. Introduction
1
2. Theoretical Background
3
2.1. Co-Occurrence Restrictions
3
2.2. Similarity
5
3. The Issue
8
3.1. Research Questions
8
3.2. Data
8
4. The Similarity Model
9
5. Hypotheses
13
6. The Lexical Analysis
15
6.1. Design
15
6.2. Results
17
6.2.1. Observed vs. Expected (O\E)
17
6.2.2. Observations
18
6.2.3. Correlation between the Lexicon and the Similarity Scale
22
6.2.4. Conclusions
23
7. The Experiments
24
7.1. The Lexical Decision Experiment
24
7.1.1. Participants
25
7.1.2. Stimuli
25
7.1.3. Procedure
26
7.1.4. Results
27
7.1.5. Discussion
28
7.2. Word-Likelihood Judgments Experiment
28
7.2.1. Participants
29
7.2.2. Stimuli
29
7.2.3. Procedure
32
7.2.4. Results
33
7.2.4.1. Pre-analysis: the Effect of C3
33
7.2.4.2. Observations
34
7.2.4.3. Comparisons to the Scales
37
8. General Discussion
39
9. Concluding Remarks
42
Appendix
45
iv
CHAPTER 1: INTRODUCTION
In Semitic languages, homorganic consonants (i.e. consonants that share place of
articulation) tend not to co-occur within the same stem. For example, verbs as datam or
kaɡam are not likely to be found in the lexicon of any Semitic language (Greenberg
1950). Previous studies (as McCarthy 1981, 1986; Frisch et al. 2004) attributed these
restrictions to similarity effects, such that the greater the similarity between two
(homorganic) consonants, the less likely they are to co-occur.
The current study examines the co-occurrence restrictions in the Hebrew verbal
system, focusing on the contribution of similarity between consonants to these
restrictions. The study focuses on the co-occurrences of C1-C2 stem consonants in the
verb classes kal (CaCaC) and pi'el (CiCeC), both in the verbal lexicon (lexical analysis)
and in the phonological systems of the speakers (psycholinguistic experiments).
Similarity between consonants was calculated based on Frisch et al.'s (2004) similarity
model (originally proposed for Arabic), adjusted according to the Hebrew consonant
inventory.
Three main questions were asked in the study:
a. What are the co-occurrence restrictions in the Hebrew verbal system?
b. What is the role of similarity in the co-occurrence restrictions?
c. Nature vs. Nurture: are co-occurrence restrictions caused by a universal constraint
or influenced by language-specific lexical factors?
The study has three main parts. Each part tested a different aspect of the cooccurrence restrictions, and then the correlations between the parts were examined. The
parts of the study are as follows:
a. Application of Frisch et al.'s (2004) model to the Hebrew consonant inventory (§4);
b. Lexical analysis of co-occurrence restrictions in the Hebrew verbal lexicon (§6);
1
c. Two psycholinguistic experiments: a lexical decision task and a judgment task.
Both experiments examine the role of co-occurrence restrictions in the speakers'
phonological system (§7).
The results show a highly significant correlation between the similarity scale
and the lexical analysis, and also between the similarity scale and the results of the
judgment experiment. A correlation was found between the lexical analysis and the
judgment experiment results as well. These findings suggest that there are similarity
based co-occurrence restrictions on stem consonants C1 and C2, both in the lexicon and
in the speakers' phonological system. However, the experiments cannot suggest
whether the influence of similarity on the grammatical system is direct, or indirect
through the lexical influences. In addition, the results suggest that place of articulation
has a major role in the restrictions (compared to other features), such that consonants
that share the major place feature are less likely to co-occur. This finding strengthens
previous claims regarding the important role of OCP-Place in co-occurrence restrictions
in Semitic languages (McCarthy 1981, 1986; Frisch et al. 2004 among others).
However, the highly significant correlation between the results and the similarity scale
proposes that not only the major place feature affects co-occurrence restrictions; if so,
we would expect to see no effect in non-homorganic pairs.
The study is organized as follows: §2 provides a theoretical background for the
study; §3 presents the main issue: the research questions and the data sources; §4
presents the accommodation of Frisch et al.'s model to Hebrew; §5 is dedicated to the
different hypotheses of the study; §6 presents the lexical analysis; §7 presents the
psycholinguistic experiments: §7.1 describes the lexical decision experiment and §7.2
describes the word-likelihood judgment experiment; §8 discusses the study's results; I
conclude in §9.
2
CHAPTER 2: THEORETICAL BACKGROUND
The study examines the correlation between co-occurrence restrictions and segment
similarity. In this chapter, I provide the theoretical background for the study: §2.1
presents co-occurrence restrictions and the OCP, and §2.2 presents previous studies on
similarity.
2.1
Co-Occurrence Restrictions
In an extensive cross-linguistic research, Greenberg (1950) observed that in Semitic
languages, there are no verbs in which the first two stem consonants
are identical (e.g. didem), and more generally, that homorganic consonants tend not to
co-occur within the same stem. McCarthy (1979, 1981, 1986) provides a theoretical
account for this phenomenon based on the Obligatory Contour Principle (OCP; Leben
1973, Goldsmith 1976), which was originally formulated for tonal systems. McCarthy
expanded the principle to root consonants in Semitic verbal systems (1979, 1981, 1986)
and it was further broadened to segments in general, features, syllables, and even
morphemes (see Yip 1998). A common definition of OCP, cited from McCarthy
(1986a:208), appears in (1).
(1) The Obligatory Contour Principle (OCP):
At the melodic level, adjacent identical elements are prohibited.
McCarthy (1986) suggests that stem consonants and vocalic patterns are independent
morphological units, located on different tiers. Since stem consonants are adjacent on
their tier, the OCP rules out any representation with adjacent identical element. Note
that the second and the third stem consonants (C2 and C3) are allowed to be identical
3
(e.g. dimem 'to bleed', kilel 'to curse', miʃeʃ 'to grope').1 McCarthy suggests that in these
verbs, the stem contains only two consonants, and the second consonant C2 spread into
the empty C3 slot. This type of verb is beyond the scope of this study.
Rose (2000) claims that these restrictions can be explained without referring to
tiers. In her view, the OCP is not restricted to adjacent consonants but depends on
proximity as well. Thus, identical consonants separated by vowels (i.e. CiVCi) also
violate the OCP constraint, though to a lesser extent than C iCi given the higher
proximity. Along this line, the restrictions on C1 and C2 will be greater than the
restrictions on C1 and C3, since C1-C3 are farther away from each other. Greenberg
(1950) indeed shows this tendency, as does Frisch et al. (2004). The current study
examines only C1-C2, and leaves proximity for further research.
Hebrew and Arabic supply evidence for these co-occurrence restrictions. Laks
(2011) shows blocking due to OCP in Hebrew and Arabic, where some verbs fail to
undergo valence-changing operations since such operation would lead to an OCP
violation. For example, dike 'to make depressed' does not undergo the valence-changing
operation to *hitdake 'to get depressed', although it is semantically possible. If such a
derivation had occurred, it would have created an OCP violation (t-d). In addition, OCP
restrictions have empirical support from psycholinguistic experiments: Frisch and
Zawaydeh (2001) for Arabic, Berent and colleagues (Berent and Shimron 1997, Berent,
Everett and Shimron 2001 among others) for Hebrew.
Bat-El (2003) claims that these restrictions are not unique to Semitic languages,
and that co-occurrence restrictions on stem consonants can be found in non-Semitic
languages as well. In English, for example, there are no monosyllabic words of the form
sCVC in which the same non-coronal consonant (i.e. labial or velar) appears in both
1
The verbs are presented in 3rd person singular past throughout.
4
sides of the vowel, for example *spep, *skik (Fudge 1969, Clements and Keyser 1983,
Davis 1984).
In Japanese, co-occurrences restrictions on homorganic consonants are found in
Yamato (native-Japanese) stems. In addition, Japanese has blocking effects due to OCP
violations. Consider, for example, the phenomenon of Rendaku - voicing of the first
consonant of the second member in a compound. For historical reasons, h alternates
with b, as in nui 'saw' + hari 'needle' → nui-bari 'sewing needle'. However, when the
stem begins with h followed by m, Rendaku is blocked in order to avoid two near
homorganic consonants, for example mai 'dance' + hime 'princess' → mai-hime 'dancing
princess', and not *mai-bime (Kawahara et al. 2006). Note that when there is a nonlabial consonant between the h and the m, Rendaku does occur (e.g. ryoori-basami
'cooking scissors', naga-bakama 'long hakama'). This finding suggests that proximity
plays a role as well. 2
McCarthy (1986) suggests that blocking due to an OCP violation is universal.
Odden (1988) stipulates that blocking differs cross-linguistically, and language differ
in the sets of relevant features for the principle. In Optimality Theory (Prince and
Smolensky 1993) this is represented by different constraint rankings in different
languages.
2.2
Similarity
Different studies (Pierrehumbert 1993, Frisch et al. 2004, Mielke 2009 among others)
have addressed the question of how segment similarity should be measured. The current
study focuses on the phonological approach that is based on articulatory phonological
features; other approaches, like those based on acoustic parameters (see, for example,
Mielke 2009), are beyond the scope of this study.
2
See Yip (1988) for more examples of OCP as process-trigger or process-blocker.
5
Pierrehumbert (1993) calculates similarity between two segments by counting
the number of feature values the segments share. Frisch et al. (2004) expand this model
to a natural-classes-based model, in which similarity value is computed for each pair of
segments by the number of natural classes they share. Thus, in Frisch et al.'s model,
similarity is computed by dividing the number of shared natural classes of two segments
by the sum of the shared and non-shared natural classes of the two segments. The
formula appears in (2).
(2)
Frisch et al.'s (2004) similarity formula
𝑠𝑖𝑚𝑖𝑙𝑎𝑟𝑖𝑡𝑦 =
𝑠ℎ𝑎𝑟𝑒𝑑 𝑛𝑎𝑡𝑢𝑟𝑎𝑙 𝑐𝑙𝑎𝑠𝑠𝑒𝑠
𝑠ℎ𝑎𝑟𝑒𝑑 𝑛𝑎𝑡𝑢𝑟𝑎𝑙 𝑐𝑙𝑎𝑠𝑠𝑒𝑠 + 𝑛𝑜𝑛 𝑠ℎ𝑎𝑟𝑒𝑑 𝑛𝑎𝑡𝑢𝑟𝑎𝑙 𝑐𝑙𝑎𝑠𝑠𝑒𝑠
By this procedure, a similarity scale for each language can be computed, based
on the contrastive features and natural classes of the language. Frisch et al. (2004) tested
the model on Arabic verb stem consonants, looking for OCP restrictions in the verbal
paradigms. First, they showed that OCP restrictions do occur in the lexicon, where
combinations of consonants with shared features are underrepresented systematically.
Next, using the above formula, Frisch et al. constructed a similarity scale based on
natural classes defined according to contrastive phonological features found in Arabic. 3
Then, the results of the lexical study were examined in light of the similarity scale. The
study showed a strong correlation between them, namely the similarity scale, based on
natural classes, successfully explaining the co-occurrence restrictions in the Arabic
lexicon. The current study will examine this model in Hebrew, by a lexical study and
psycholinguistic experiments.
3
[±consonantal], [±sonorant], [±continuant], [±acute], [±strident], [±nasal], [±lateral], [labial], [coronal],
[dorsal], [pharyngeal], [radical], [±anterior], [±back], [±voice], [±spread glottis], and [±constricted
glottis].
6
Next, the question arises as to which features are relevant to similarity. Rose and
Walker (2004) claim that [sonorant], [continuant] and place features are the most
important in computing similarity. Kawahara (2007) suggests that manner features
(such as palatalization, voicing, nasalization, and continuity) contribute to similarity
more than place features. This is compatible with claims that manner features are
perceptually more salient, and that speakers tend to rely on acoustic parameters while
calculating similarity (see Mielke 2009). Kaisse (1988) claims that the OCP applies to
feature groups and not just to single features, and so provides direct evidence for
Feature Geometry, which argues for feature hierarchies (Clements 1985, Sagey 1986,
Clements and Hume 1995, see also McCarthy 1988). Along this line, Padgett
(1995:181) revised the definition of the OCP to take into account feature hierarchies
(3):
(3) The Obligatory Contour Principle (OCP):
At the melodic level, adjacent identical elements F F are prohibited, iff all
subsidiary features stipulated for F are also identical.
Along the line of Frisch et al.'s (2004) study, the current study examines natural
classes and does not test the influence of every feature individually. The only feature
that is examined separately is place of articulation, following the importance of OCPPlace as suggested by previous studies (see §2.1). Further research is needed in order
to test the influence of other features and the correlations with feature hierarchies.
7
CHAPTER 3: THE ISSUE
3.1
Research Questions
The goal of this study is to examine the co-occurrence restrictions in the Hebrew verbal
system, focusing on the contribution of similarity between consonants to these
restrictions. Three main questions are addressed:
a. What are the co-occurrence restrictions in the Hebrew verbal system? While
previous studies (McCarthy 1981, 1986; Pierrehumbert 1993; Frisch et al. 2004)
focused on OCP-Place, shared place as a necessary feature for the effect, the current
study attributes equal weight to all features.
b. What is the role of similarity in the co-occurrence restrictions?
c. Nature vs. Nurture: are co-occurrence restrictions caused by a universal constraint
or influenced by language-specific lexical factors?
3.2
Data
The study examines the co-occurrence restrictions in two sources of data:
a. The Hebrew verbal lexicon, focusing on C1-C2 stem consonants in verb classes kal
(CaCaC) and pi'el (CiCeC). I use the list of verbs from the Even-Shoshan dictionary
(edition 1970 with completions from 1983). 4
b. Two psycholinguistic experiments, a lexical decision task and a word-likelihood
judgment task, to examine the role of similarity in the speakers' phonological system.
All the data were analyzed with respect to Frisch et al.'s (2004) model. This is,
inter alia, since Arabic and Hebrew are historically related (Schwarzwald 2002 among
many others), and Frisch et al.'s model successfully explained the OCP effect in Arabic
verbs.
4
Many thanks to Shmuel Bolozky for an electronic version of the verb list.
8
CHAPTER 4: THE SIMILARITY MODEL
The first part of the study applies Frisch et al.'s (2004) similarity model to the Hebrew
consonant inventory. As discussed in §2, the model computes a single similarity value
(from 0 to 1) for each pair of consonants, and the computation is based on the natural
classes to which the consonants belong. The classes are defined according to the
language contrastive features. The Hebrew consonant inventory appears in (4), and the
set of contrastive features I used appears in (5).
(4)
Hebrew consonants
Bilabial
Labio-
Alveolar
dental
Plosive
p
f
Palatal
v
Uvular
t
d
s
z
ɡ
k
ʃ
x5
ʦ
Affricate
Nasal
m
n
Lateral
l
Approximant
(5)
Velar
alveolar
b
Fricative
Palato-
ʁ6
j
Set of contrastive features
p
b
m
f
v
t
d
s
z
ʦ
ʃ
n
j
k
ɡ
x
ʁ
[±sonorant]
-
-
+
-
-
-
-
-
-
-
-
+ + +
-
-
-
+
[LAB]
√ √
√
√ √
l
√ √ √ √ √ √ √ √ √
[COR]
√ √ √ √
[strident]
[±anterior]
+ + + + +
-
+ +
√ √ √ √
[DOR]
[±continuant]
-
-
[±voice]
-
+
-
+ +
-
-
+ +
-
+
-
-
+
-
-
-
+
5
+
-
+ +
-
-
+ +
-
+
-
Bolozky and Kreitman (2007) consider the Hebrew dorsal fricative to be uvular. Nevertheless, its exact
place of articulation has no consequences for the current study, since minor place features for the dorsals
are not contrastive in Modern Hebrew.
6
The Hebrew rhotic is considered a uvular approximant with certain frication (Bolozky and Kreitman
2007), IPA: [ʁ̞]. Hereinafter it will be transcribed as ʁ.
9
I excluded borrowed consonants (ʒ, ʤ, ʧ and w) from the analysis due to their
rare appearance in the verbal system, and the glottals (ʔ and h) due to their tendency to
be omitted in Modern Hebrew.7 This was done mainly for the sake of comparison
between the lexical analysis and the experiments' results. The feature system I used is
based on binary (6a) and unary (6b) values:
(6)
Features:
a. Binary: [±sonorant], [±continuant], [±voice], [±anterior]
b. Unary: place features: [LAB] (labials), [COR] (coronals), [DOR] (dorsals);
[strident]
Two issues should be noted: First, [±voice] is relevant only for obstruents; it is
not contrastive among sonorants, and it has been claimed that the voice feature of the
sonorants is inherent in them and therefore differs from the voice feature of the
obstruents (Rice 1993). Second, I refer to stridency as a unary feature, such that the
value [-strident] is not a part of the system. The stridents in Hebrew show a common
phonological behavior – they undergo metathesis in binyan hitpa'el (e.g. hit-sapeʁ →
histapeʁ 'to have a haircut'). Therefore, [strident] is relevant for the Hebrew
phonological system.8 The non-strident consonants, on the other hand, do not show any
common phonological behavior in Hebrew, and [-strident] is also not necessary for
minimal distinction between consonants in the system. For these reasons, I excluded
the [-] value of this feature from the analysis. 9 The natural classes were defined based
on this feature system, down to the level of singletons.
7
The question of whether the phonological system of Hebrew represents glottals is beyond the scope of
this paper.
8
Note that [strident] is more of an acoustic rather than articulatory feature. Nonetheless, it is widely used
(also in Frisch et al.'s model) and explains different phonological processes in different languages.
9
Frisch et al. (2004) used [-strident] only for non-strident coronal fricatives. Hebrew has no such
consonants in its inventory.
10
Based on this phonological feature system, I computed the similarity value for
each pair of consonants, using Frisch et al.'s (2004) formula. The formula was presented
in (2), and is repeated in (7). The natural classes and the similarity values were
calculated via a Microsoft Excel macro.10
(7)
Frisch et al.'s (2004) similarity formula
𝑠𝑖𝑚𝑖𝑙𝑎𝑟𝑖𝑡𝑦 =
𝑠ℎ𝑎𝑟𝑒𝑑 𝑛𝑎𝑡𝑢𝑟𝑎𝑙 𝑐𝑙𝑎𝑠𝑠𝑒𝑠
𝑠ℎ𝑎𝑟𝑒𝑑 𝑛𝑎𝑡𝑢𝑟𝑎𝑙 𝑐𝑙𝑎𝑠𝑠𝑒𝑠 + 𝑛𝑜𝑛 𝑠ℎ𝑎𝑟𝑒𝑑 𝑛𝑎𝑡𝑢𝑟𝑎𝑙 𝑐𝑙𝑎𝑠𝑠𝑒𝑠
For example, the similarity value for p and b is calculated as follows: they share
7 natural classes, and do not share 8 classes, namely, there are 8 natural classes in which
only one of them is a member (see list in (8)). Therefore, the similarity value for the
pair p,b is:
(8)
7
7+8
= 0.467.11
Shared and non-shared natural classes for the pair p-b:
a. Shared classes: [-son] = {p,b,f,v,t,d,s,z,ʦ,ʃ,k,ɡ,x}, [-son, LAB] = {p,b,f,v}, [son, LAB, -cont] = {p,b}, [-son, -cont] = {p,b,t,d,ʦ,k,ɡ}, [LAB] =
{p,b,m,f,v}, [LAB, -cont] = {p,b,m}, [-cont] = {p,b,m,t,d,ʦ,n,k,ɡ}.
b. Non-shared classes:
i.
p and not b: [-son, LAB, -cont, -voice] = {p}, [-son, LAB, -voice] =
{p,f}, [-son, -cont, -voice] = {p,t,ʦ,k}, [-son, -voice] = {p,f,t,s,ʦ,ʃ,k,x};
ii. b and not p: [-son, LAB, -cont, +voice] = {b}, [-son, LAB, +voice] =
{b,v}, [-son, -cont, +voice] = {b,d,ɡ}, [-son, +voice] = {b,v,d,z,ɡ}.
Note that although there is only one feature that distinguishes these two
segments, [±voice], they have eight non-shared natural classes. Since the calculation is
10
Many thanks to Chen Gafni for programming the Natural Classes Generator on Microsoft Excel
platform. The full list of natural classes appears in Appendix A.
11
I treat similarity as symmetrical, and (a)symmetry is beyond the scope of this paper. See also §9.
11
based on natural classes and not on features directly, the distance between them in this
model is more notable.
Table (9) presents the similarity values for the consonants in Hebrew, and table
(10) the most similar pairs on the scale. A full list appears in Appendix B.
(9)
p
Similarity values
b
m
f
v
t
d
s
z
ʦ
ʃ
n
l
j
k
ɡ
x
ʁ
p
1.000 0.467 0.200 0.313 0.167 0.250 0.143 0.067 0.037 0.192 0.074 0.050 0.000 0.000 0.313 0.176 0.100 0.000
b
1.000 0.200 0.167 0.313 0.136 0.263 0.032 0.077 0.107 0.036 0.050 0.000 0.000 0.167 0.333 0.048 0.000
m
1.000 0.063 0.063 0.050 0.053 0.000 0.000 0.038 0.000 0.214 0.059 0.059 0.063 0.067 0.000 0.077
f
1.000 0.429 0.091 0.045 0.192 0.125 0.071 0.217 0.000 0.050 0.050 0.111 0.056 0.313 0.063
v
1.000 0.043 0.095 0.107 0.227 0.034 0.120 0.000 0.050 0.050 0.053 0.118 0.167 0.063
t
1.000 0.500 0.296 0.192 0.700 0.185 0.200 0.087 0.042 0.263 0.150 0.087 0.000
d
1.000 0.172 0.304 0.375 0.107 0.211 0.091 0.043 0.150 0.294 0.043 0.000
s
1.000 0.520 0.414 0.500 0.069 0.185 0.103 0.069 0.034 0.185 0.037
z
1.000 0.233 0.296 0.080 0.217 0.120 0.038 0.083 0.120 0.043
ʦ
1.000 0.226 0.154 0.069 0.033 0.200 0.115 0.069 0.000
ʃ
1.000 0.037 0.115 0.208 0.077 0.038 0.208 0.042
n
1.000 0.313 0.167 0.053 0.056 0.000 0.063
l
1.000 0.467 0.000 0.000 0.048 0.200
j
1.000 0.000 0.000 0.048 0.200
k
1.000 0.462 0.313 0.063
ɡ
1.000 0.176 0.067
x
1.000 0.200
ʁ
1.000
(10) Most similar pairs12
pair
12
similarity value
1 ʦ-t
0.7
2 s-z
0.52
3 t-d
0.5
3 s-ʃ
0.5
4 p-b
0.467
4 l-j
0.467
5 k-ɡ
0.462
6 f-v
0.429
7 ʦ-s
0.414
Excluding identical consonants, which have a similarity value of 1.
12
CHAPTER 5: HYPOTHESES
After calculating the similarity scale, I examined the correlation between co-occurrence
restrictions and the similarity value of the first two stem consonants of the verbs. The
correlation was examined on two levels: the lexical level and the phonological level (in
the word-likelihood judgment experiment). The lexical analysis and the wordlikelihood judgment experiment (as well as their correlation) may have different results,
which would lead to different conclusions.
a.
Lexicon 1 - Experiment 1: In this scenario, the same similarity-based co-occurrence
restrictions are found both in the lexicon and in the speakers' judgments. Such results
may indicate that similarity plays a role in co-occurrence restrictions in Hebrew.
However, it will not suggest whether the influence of similarity on the grammatical
system is direct, or indirect through the lexical influences.
b.
Lexicon 0 - Experiment 0: In this scenario, similarity-based co-occurrence
restrictions are not found in Hebrew at all. Based on previous studies on OCP in Arabic
(Greenberg 1950; McCarthy 1981, 1986; Frisch et al. 2004), this is the least plausible
scenario.
c.
Lexicon 1 - Experiment 0: In this scenario, co-occurrence restrictions are found in
the lexicon but not in the speakers' judgments. Such results may indicate that OCP was
active in previous stages of Hebrew (many verbs in the Modern Hebrew lexicon have
origins in Biblical Hebrew or in Mishnaic Hebrew), but nowadays the constraint is no
longer active.
d.
Lexicon 0 - Experiment 1: In this scenario, co-occurrence restrictions are not found
in the lexicon but are found in the speakers' judgments. Such results may indicate that
OCP is not active in the lexicon, but the speakers are sensitive to it, nevertheless. A
13
plausible explanation would be that the OCP is a universal principle, which the
sensitivity to it does not come from the segmental distributions in a specific language.
This hypothesis would be supported by studies such as Berent (2008), who found that
speakers of Korean, which does not have clusters, are nonetheless sensitive to SSG
(Sonority Sequencing Generalization) violations.
14
CHAPTER 6: THE LEXICAL ANALYSIS
The second part of the study analyzes the Hebrew verbal lexicon, based on Frisch et
al.'s (2004) model. Unlike Frisch et al.'s analysis of Arabic, the current study examines
not only homorganic consonants, but also every other possible combination of
consonants. The list of verbs is taken from Even-Shoshan dictionary (edition 1970 with
completions from 1983), and I used Barkali (1964) for full paradigms.
6.1
Design
The study focuses on two verb classes (binyanim): kal (CaCaC) and pi'el (CiCeC). I
chose these classes since they show different behaviors throughout the paradigm: while
in pi'el C1-C2 are separated by one vowel throughout the inflectional paradigm, in kal
the future paradigm gives rise to adjacent C1-C2 (see Appendix C for sample
paradigms). Thus, it is possible to examine whether this difference in distance has an
impact on the results.
The analysis was conducted from a synchronic point of view, with the aim of
comparing its results with the psycholinguistic experiments results. Thus, I analyzed
only regular verbs (shlemim, see Zadok 2012), in which all three-stem consonants
appear synchronically throughout the paradigm. Therefore, I excluded from the analysis
the glottals (ʔ, h), v (orthographic: va"v; historical: w, synchronic: v) and j. For example,
the verb ʃama(ʕ) 'to hear' historically ended with a ʕ and traditionally is considered as
part of the regular verbs. However, nowadays final ʕ is omitted, so the verb is in a
template of CaCa. Along the lines of Zadok (2012), this verb is not part of the regular
verbs, and therefore was omitted from the analysis. In addition, consonants that have
undergone a historical change are considered by their synchronic status. Thus, historical
15
tʕ is considered as t; historical q as k; historical ħ as x. Overall, 779 verbs in kal and 678
verbs in pi'el were analyzed.13
The analysis takes into account paradigms (and not only stems), such that each
verb appears in three forms drawn from the past, present and future paradigms (all
forms are in 3rd, singular, masculine). In this way, alternations throughout the paradigm
can be considered, including differences in the distance between C1 and C2 (e.g. ʃamaʁ
'he saved' – C1VC2, jiʃmoʁ 'he will save' C1C2), and the spirantization of b,p, and k to
v,f, and x respectively (e.g. katav 'he wrote', jixtov 'he will write'), see Appendix C for
sample paradigms. For example, consider the pairs d-ʃ and b-ʃ. Each pair has only one
verb in pi'el: diʃen 'to fertilize' for d-ʃ and biʃel 'to cook' for b-ʃ. However, due to
spirantization alternations, d-ʃ has three occurrences in the lexicon: diʃen-medaʃenjedaʃen 'to fertilize Past-Present-Future', while b-ʃ has only one: biʃel 'to cook Past'. The
present and future forms, mevaʃel and jevaʃel respectively, contain v instead of b due to
spirantization, and thus contribute to the pair v-ʃ.
After selecting the relevant verbs, I counted how many forms there were for each
C1-C2 pair. For example, consider the pair d and m: 24 forms in the tested lexicon begin
with this pair: 9 forms in kal (6 for d-m and 3 for m-d), and 15 forms in pi'el (6 for d-m
13
A few comments are addressed:
a. In binyan kal, future tense, an epenthetic vowel may be inserted after a synchronic x that historically
originated in ħ, for example jaxaʃov~jaxʃov 'he will think'. However, synchronically, speakers tend
not to epenthesize a vowel in these cases (i.e. stick to the standard form), and evidence for variation
between the two forms appears even in the Bible (e.g. taħbol~taħavol 'you ms. will take as pledge',
Exodus, 22;25, Deuteronomy, 24;17, respectively). Therefore, I included these forms in the analysis.
b. Verbs in pi'el with C2 ʁ have (normatively, at least) a vocalic pattern of CeCeC (e.g. seʁek 'he
combed'), and not the standard CiCeC, due to historical changes. Since it is plausible to assume that
the different vocalic pattern does not influence the similarity between C1-C2, I included these forms
in the analysis.
c. I included in the analysis verbs in kal with C1 n, although in some of these verbs the n is deleted in
the future form, for example nafal-jipol (and not *jinpol; p~f alternation due spirantization) 'he
fell\will fall' (respectively). In these verbs, only past and present forms were taken into account.
16
and 9 for m-d). For example, the triplet madad-moded-imdod 'to measure Past-PresentFuture' represent three instances.
Next, I compared the observed (O) results to the expected (E) ones (O/E), based
on consonant frequencies, in order to examine what (if any) the co-occurrence
restrictions on C1-C2 are. According to previous studies on OCP in Arabic (Greenberg
1950, Frisch et al. 2004 among others), there is a solid basis to assume that some
restrictions will be shown in the Hebrew lexicon as well. After calculating the O/E ratio,
I compared the results to the similarity model, in order to examine if a correlation can
be found between co-occurrences and the similarity values.
Two questions were asked:
a. Observed vs. Expected (O/E): Are there any differences between the observed and
the expected occurrences of each consonant pair in the lexicon? In other words, is
the number of occurrences of each pair similar to what would be obtained if the
lexicon were random?
b. Correlation with the similarity scale: Is there any correlation between the
occurrences in the lexicon and the similarity scale?
6.2
Results
6.2.1 Observed vs. Expected (O/E)
First, in order to look for differences between the observed and expected cooccurrences, a chi-square test was conducted for each verb class (binyan) separately (a
full list of the occurrences appears in Appendix D). In one calculation, the order of the
consonants was taken into account (e.g. d-t was calculated separately from t-d) and in
the other the order was not inserted as a factor (e.g. d-t and t-d were calculated together
as one item). In addition, the tests took into account the frequency of each tested
consonant in the corpus (a full list of the frequencies appears in Appendix E). Thus, the
17
expected results refer to what would be expected if the single consonants were
combined to pairs of C1-C2 randomly.
The results show highly significant differences between the observed and
expected, in all the tested cases: binyan kal: with no consideration of order: χ2 = 970.24,
p < 0.0001; including consideration of order: χ2 = 1069.47, p < 0.0001; binyan pi'el:
with no consideration of order: χ2 = 767.8215, p < 0.0001; including consideration of
order: χ2 = 912.03, p < 0.0001. These results show that there is a gap in the lexicon
between the observed and expected consonant co-occurrences. Based on previous
studies (see §2), it is plausible to assume that similarity is one of the factors that causes
this gap, and the next sub-sections test this assumption.
6.2.2 Observations
Next, a few interesting observations can be made by looking at the bottom of the
occurrences list. Since a significant gap between observed and expected was found also
in the list that do not take order and binyanim into account, the following sub-section
deals with the combining list. Thirty pairs of consonants do not appear in the lexicon at
all. Table (11) presents them and their similarity values on the similarity scale.
18
(11) No occurrences at the lexicon (in brackets: place on the list, out of 66)14
Pair
Similarity
Pair
Similarity
Pair
Similarity
b-b
1
z-z
1
d-z
0.304 (11)
d-d
1
ʦ-t
0.7 (1)
m-b
0.2 (23)
f-f
1
s-z
0.52 (2)
m-p
0.2 (23)
ɡ-ɡ
1
t-d
0.5 (3)
ʁ-l
0.2 (23)
p-p
1
b-p
0.467 (4)
b-f
0.167 (28)
ʁ-ʁ
1
k-ɡ
0.462 (5)
p-v
0.167 (28)
s-s
1
f-v
0.429 (6)
m-f
0.063 (51)
ʃ-ʃ
1
ʦ-s
0.414 (7)
m-v
0.063 (51)
t-t
1
b-v
0.313 (10)
ʦ-ʦ
1
n-l
0.313 (10)
v-v
1
p-f
0.313 (10)
A few observations can be made. First, it is notable that the most similar pairs
indeed do not appear in the lexicon. Identical consonants tend not to co-occur (except
for five pairs, which will be discussed in the next paragraph), and places 1-7 in the most
similar pairs (except for one) do not appear in the lexicon as well. Note that the pair sʃ is also ranked third in the similarity scale with a similarity value of 0.5. It has nine
occurrences in the lexicon, three verbs in three tenses each, from which only one verb
is in use (but with low frequency) in Modern Hebrew (ʃisef 'to slit someone's throat').15
Five identical consonant pairs have more than zero occurrences in the tested
lexicon, summarized in table (12).
14
15
Identical consonants have a similarity value of 1, and are not considered in the numbering.
The other two verbs are ʃasaf 'to split (Middle Ages Hebrew)' and ʃasas 'to despoil (Biblical Hebrew)'.
19
(12) Identical consonant pairs with occurrences in the lexicon
Pair
Occurrences
Different Verbs
Details
k-k
1
1
One form – normative: kikev 'to star',
colloquial: kixev.
l-l
3
1
lilev-melalev-jelalev 'to strengthen with a palm
branch Past-Present-Future' – unused in
Modern Hebrew.
n-n
3
1
nines-menanes-jenanes 'to make smaller PastPresent-Future' – unused in modern Hebrew.
m-m
6
2
mimen-memamen-jemamen 'to finance PastPresent-Future' and mimeʃ-memameʃ-jemameʃ
'to realize Past-Present-Future' – both are in
used in Modern Hebrew.
x-x
15
9
All of them contained historically one
spirantized k (x) and ħ (in Modern Hebrew –
x); therefore, the identical x-x do not appear
throughout the paradigm. They are used very
rarely or not at all in Modern Hebrew (for
example xaxaʁ 'to lease'). 16
Overall, there are only two verbs with originally identical C 1-C2 that are in use
in Modern Hebrew, both with m: mimen-memamen-jemamen 'he financed Past-PresentFuture' and mimeʃ-memameʃ-jemameʃ 'he realized Past-Present-Future'. The others are
not part of the commonly used lexicon (at least some of them for semantic reasons; lilev
is not used since it is not customary to use a palm branch as a strengthening device
nowadays).
The largest group of verbs with identical C1-C2 contains x in both positions.
However, all these cases are a combination of a historical ħ and spirantized allophone
The Hebrew orthography shows evidence to the historical change: חstands for the historical ħ and כ
stands for k, or for x which is a historical allophone of k.
16
20
of k. Hence, historical reasons are responsible to the identical C1-C2 in the synchronic
lexicon; originally, C1 and C2 were different from each other in these verbs.
Another interesting finding is the verb kikev 'he starred'. This verb is widely used
in Modern Hebrew, but speakers pronounce it kixev. It is plausible to assume that this
pronunciation is influenced by several factors: first, this verb is strongly related to the
noun koxav 'a star', and in fact, the first four discussed forms are denominatives (also
lilev-lulav 'palm branch', nines-nanas 'midget', mimen-mamon 'money'). Thus, under
word-based morphology, the verb was derived from the word koxav itself, and the
speakers want to keep maximum faithfulness between the noun and the verb (see
Aronoff 1976 and Bat-El 1994). Second, changing the second k into x reduces the
similarity between the two. The change is possible because it would not cause a change
in orthography (the letter כallows both consonants, k and x), and because x is a phoneme
in Modern Hebrew and can appear in a non-spirant position (i.e. a position in which
there are no conditions for spirantization).
The results so far show a correlation between similarity and co-occurrence
restrictions. Nonetheless, it can be noted that all the pairs that do not appear in the
lexicon (except for ʁ-l, which will be discussed in the next paragraph), also share a
major place of articulation. Therefore, the co-occurrence restriction can be connected
to OCP-Place violation, as was suggested for Arabic and for Semitic languages in
general (McCarthy 1979, 1981, 1986; Frisch et al. 2004 among others). Moreover, the
list of zero occurrences also contains homorganic pairs that are rated low on the
similarity scale, for example m-f and m-v (both 0.063, place 51 from 66), a finding that
strengthens the claim regarding the major role of place features in the co-occurrence
restrictions.
21
The pair ʁ-l acts like homorganic pairs that do not appear in the lexicon. Since
the Hebrew rhotic is considered uvular and not coronal (Bolozky and Kreitman 2007),
this finding is slightly surprising, and can be related to the special status of the Hebrew
rhotic. Note that the pair ʁ-x, which share a major place of articulation of dorsals, has
72 occurrences in the lexicon. Both ʁ-x and ʁ-l have a similarity value of 0.2 in the
similarity model, a fact that adds more to the puzzle.17
To conclude, the 30 pairs that have zero occurrences in the tested lexicon suggest
that similarity plays a role in co-occurrence restrictions. Almost all the identical C1-C2
and the most similar pairs (according to the model) do not appear in the lexicon.
However, it is notable that all the pairs (except for l-ʁ) that do not appear in the lexicon
are homorganic. This suggests that place features have a special role in the cooccurrence restrictions.
6.2.3 Correlation between the Lexicon and the Similarity Scale
Next, a Zero-Inflated Poisson Regression model was built in order to test statistically
the correlation between the lexicon and the similarity scale. The similarity factor in the
model is expected to be negative, since the more consonants are similar to each other
(their similarity value is closer to 1), the less they are expected to co-occur in the
lexicon. The model shows that the similarity factor is significantly negative in all cases:
binyan kal: Estimate = -0.112, SD = 0.023, p < 0.0001; binyan pi'el: Estimate = -0.147,
SD = 0.021, p < 0.0001; kal and pi'el together: Estimate = -0.092, SD = 0.014, p <
0.0001.18
17
There is evidence that the Biblical Hebrew rhotic is dorsal (it behaves like pharyngeals and glottals,
e.g. cannot undergo gemmination and cause vowel lowering). However, some studies suggest that the
Biblical rhotic has two variants – coronal and dorsal – and in some Mizrahi Jews communities the rhotic
is pronounced as coronal (Blau 2010).
18
For simplicity of the statistical calculations, in the calculations for pi'el and for the sum of kal and pi'el
only the second consonant and the similarity value were taken into account.
22
The similarity factor in these models indicates the correlation between the
appearance in the lexicon and the similarity scale: the more two consonants are similar
to each other, the less their chances to co-occur as C1-C2 in a Hebrew verb.
6.2.4 Conclusion
The lexical analysis tested pairs of stem consonants C1-C2 in the Hebrew verbal lexicon,
and shows a significant difference between observed and expected (O/E) cooccurrences. Assuming that languages are systematic, this result suggests that there are
factors that shape the lexicon and impose restrictions. Statistical models suggest that
similarity (based on Frisch et al.'s 2004 model) is one of these factors. The next chapter
tests the role of similarity in co-occurrence restrictions from a synchronic point of view,
focusing on the phonological system of Hebrew speakers.
23
CHAPTER 7: THE EXPERIMENTS
In chapter 6, I described the lexical analysis, which looked for similarity based cooccurrence restrictions. The results show that the gaps in the lexicon are found in
correlation to the similarity scale. However, a lexical analysis cannot supply a complete
account for co-occurrence restrictions. First, this kind of analysis can only highlight
what exists, and not ask directly about what is absent. Second, the tested lexicon
represents an upper bound of the Hebrew speaker's vocabulary (not all speakers are
familiar with all forms in the lexicon), thus in any case it cannot directly represent the
phonological knowledge of a particular Hebrew speaker. Third, lexicons have lots of
exceptions for example due to historical residue.
In order to complete the picture, an experiment on nonce-verbs in Hebrew was
conducted. In the lexical decision experiment the participants were asked to make a
lexical decision about verbs and non-verbs, and in the word-likelihood judgment
experiment the participants were asked to give word-likelihood ratings for nonce-verbs
in Hebrew.
7.1
The Lexical Decision Experiment
Following the observation that C1 and C2 obey the OCP-Place in Semitic languages
(Greenberg 1950; McCarthy 1981, 1986), I conducted a psycholinguistic experiment
that examined OCP-Place effects in the Hebrew verbal system (Yeverechyahu 2012).
The results indicate that Hebrew speakers are sensitive to the OCP-Place in the verbal
system, and that sharing place features (i.e. homorganic consonants) is a sufficient
condition for the violation.
24
7.1.1 Participants
33 participants took part in the experiment (18 females, 15 males). All of them were
native Hebrew speakers who were born in Israel, between ages 21 and 29 (mean age
25, SD=2.21). None of them had studied Linguistics academically. Two additional
participants whose mean RTs were greater than the total participants' mean in more than
two standard deviations were discarded from the analysis (Mtotal=1206ms, SD=325).
7.1.2 Stimuli
The stimuli were 30 Hebrew verbs and 30 nonce-verbs in a legal Hebrew verb template.
All items were in binyan pi'el, 3rd person, masculine, singular, in past tense, namely in
the template CiCeC (e.g. ɡisem nonce-verb). In all verbs, C2 and C3, as well as C1 and
C3, did not share place of articulation or manner of articulation, in order to focus on the
OCP effect in C1 and C2 alone. In addition, the stimuli included only regular verbs
(shlemim, see Zadok 2012 and §6.1) in which all three stem consonants appear
synchronically throughout the paradigm, and nonce-verbs that look like regular verbs.
The stimuli were selected with the aid of Barkali's (1964) Hebrew verbs dictionary.
The 30 nonce-verbs were divided to five groups, as follows (a full stimuli list
appears in Appendix F):
a. Non-shared features: C1 and C2 were different in place, manner and voice (e.g.
ɡisem);19
b. Place: C1 and C2 shared the place of articulation (coronals) and differed in manner
and voice (e.g. disem);
c. Place and Manner: C1 and C2 shared place and manner of articulation (coronal
fricatives-stridents) and differed in voice (e.g. zisem);
19
Sonority was not taken into account.
25
d. Place and Voice: C1 and C2 shared place of articulation and voice (voiceless
coronals) and differed in manner (e.g. tisem);
e. Identical C1-C2: C1 and C2 are identical (e.g. sisem).
In all the verbs that shared one or more features, the shared features were
constant (coronal for place, stridency for manner, and voiceless for voice).20 Referring
to Frisch et al.'s (2004) model, this was done in order to avoid differences in the
similarity between different groups (i.e. two coronals may be less similar to each other
than two labials, since there are more natural classes of coronals in the language than
of labials).
Since the study focuses on auditory similarity and not visual similarity, all the
stimuli were presented auditorily. This was done in order to focus on the auditory
channel, and avoid orthographic or visual influence. The stimuli were recorded by a 30year-old male native speaker of Hebrew.
7.1.3 Procedure
The experiment was designed using the E-prime software (2.0). The participants sat in
front of an Asus Eee mini-laptop equipped with earphones, and heard different stimuli.
They were asked to determine whether each stimulus was an existing Hebrew verb.
"Existing verb" responses were given by pressing 1 and "nonce-verb" responses by
pressing 0, such that opposing responses were made using different fingers and hands.
A short training block was passed at the beginning of the experiment to ensure
that the participants understood the task. The training block contained ten stimuli (five
existing verbs, five nonce-verbs), and the participants were given feedback (a smiley or
a disappointed face icon) following each response.
20
Except for two nonce-verbs with identical segments, which were affricates and not fricatives (ʦ), in
order to reach six different nonce-verbs.
26
The order of the items in the experiment and in the training was randomized
across subjects. Accuracy and reaction times (hereinafter: RTs) measured from stimulus
onset were collected.21 Each subject was tested individually, and each session lasted
approximately five minutes.
7.1.4 Results
The results suggest that the subjects are sensitive to the OCP-Place constraint. Accuracy
for all nonce-verbs was extremely high (99.19% correct answers), errors were excluded
from the RT analysis. RTs for the non-homorganic C1-C2 among the nonce-verbs were
significantly greater than the RTs for the homorganic C1-C2 (t(34)=5.99, p<0.0001), as
can be seen in Figure (13). Thus, the subjects needed more time to decide that nonhomorganic C1-C2 nonce-verbs were not part of their lexicon. This finding suggests that
when C1 and C2 are homorganic, the gap in the lexicon is systematic and predicted by
the OCP-Place constraint. The OCP-Place provides the subjects with a cue that these
verbs are less likely to be Hebrew verbs.
(13) Mean RT: homorganic\non-homorganic C1-C2 (ms)
21
An ANOVA test reveals that the sound samples were not statistically different in duration
(F(5,24)=2.36, p=0.08).
27
However, the differences among the five stimulus groups which violated OCPPlace to different degrees were not significant (F(3,128)=0.61, p=0.61). It seems that
sharing the place feature, namely homorganic C1 and C2, is a sufficient condition to
determine quickly that the stimulus is a nonce-verb. This finding is compatible with
McCarthy's (1979, 1981, 1986) claim that the shared place of C1 and C2 causes OCPPlace violation in Semitic stems.
7.1.5 Discussion
The lexical decision experiment sheds light on the role of the OCP in the Hebrew verbal
system, and opens the door for further research on the topic. Particularly, the experiment
focuses on homorganic consonants (as in Frisch et al. 2004), and it raises the question
of whether co-occurrence restrictions will also be observed among non-homorganic
consonants that do share some features (voice or manner). For example, in the nonceverb dibem, C1 d and C2 b share voice ([+voice]) and manner (stops), but not place
(coronal and labial, respectively). What will be the effect of the similarity between C 1
and C2 in this case? Second, the experiment focused on division to place, manner and
voice, without looking into phonological features (as [±son], [±cont] etc.) and natural
classes. Since co-occurrence restrictions may be phonological by nature (and not purely
phonetic), it is interesting to address the issue from a more phonological point of view,
which takes into account phonological features and natural classes. Frisch et al.'s (2004)
model is based on such properties, and therefore will be suitable for analysis the results.
7.2 Word-Likelihood Judgment Experiment
In light of the results of the lexical decision experiment, a word-likelihood judgment
experiment was conducted. The aim of this experiment is to broaden the lexical decision
experiment by considering any C1-C2 combination, and comparing the results to the
similarity scale.
28
7.2.1 Participants
138 participants participated in the experiment (79 females, 59 males). All of them were
Hebrew native speakers who were born in Israel, between ages 20 and 40 (mean age
27, SD=3.84). 14 of them were BA Linguistics students, but none of them had taken
advanced courses in morphology or phonology.
7.2.2 Stimuli
The stimuli in the experiment were 331 Hebrew nonce-verbs in binyan kal, 3sg past, in
the template of CaCaC (e.g. dadam). All of them are non-existing verbs in Hebrew.
However, they were put in a verb template in order to cue the participants to consider
them as potential Hebrew verbs, so I expected that phonological factors (and not
morphological) would affect the participants' judgments.
In order to make the experiment in a reasonable length, I focused only on one
verb class, binyan kal. Testing all the consonant combinations in both kal and pi'el
would have made the experiment too long, and would have made it harder for the
participants to be focused during the entire session. Since both kal and pi'el showed
sensitivity to similarity effects in the lexical analysis, I decided to focus in the current
study on kal alone. I preferred kal over pi'el since the spirantization in the kal's paradigm
(see Appendix C) allows us to test more consonants, as there are no f or v in the base
forms of binyan pi'el.22
As with the lexical research, the experiment focuses on the similarity between
C1 and C2, while ignoring C3. The stimuli's stem consonants were selected in the
following way:
22
Except for several nominatives, for example fiʃel 'to screw up' from the word faʃla 'mistake' (borrowed
from Arabic).
29
C1 and C2: As in the lexical analysis, the stimuli included all Hebrew consonants
in the shlemim paradigm (Zadok 2012), with the correct spirantization restrictions (see
§6.1). Thus, the consonants b and p appeared as stops in C1 and as fricatives v and f in
C2 (respectively). For example, stimuli contained nonce-verbs such as bafat, but not
fabat. k and x could appear in both positions: in C1 (non-spirant position) k represents
alternating k or non-alternation k (historical q), and x represents only non-alternating x
(historical ħ). In C2 (spirant position), k represents only non-alternating k, and x
represents alternating x or non-alternation x. Overall, 17 different consonants were
examined, the same as in the similarity scale and the lexicon analysis. 23 In this way, the
relations between the similarity scale, the lexical research, and the experiment results
can be tested.
In order to make the experiment shorter, the stimuli contained each consonant
pair in only one order. For example, the pair k and l appeared as k-l and not as l-k. The
order of each pair was chosen to be the one in which there are more verbs, as found in
the lexical research. For example, there are 17 occurrences of k-l and 10 occurrences of
l-k, and therefore the stimuli in the experiment contained k-l and not l-k. In many cases,
the more frequent order in the lexicon was the order in which C1 is less sonorous than
C2. This is an interesting point that may indicate the role of sonority in co-occurrence
restrictions, and it opens a door for further research. Based on this observation, for pairs
that have the same number of occurrences in the lexicon in each order (usually 0), I
chose the order in which C1 was less sonorous than C2.
C3: The role of C3 and its similarity to C1 and C2 was not tested in this study.
Nevertheless, it is plausible that C3 influences co-occurrences as well, and therefore it
23
Except for j, which was considered in the similarity scale since it is a Hebrew consonant, but was
eliminated from the lexical analysis and the experiment since it causes changes in the paradigm.
30
should be neutralized. In order to control for the effect of C 3, each C1-C2 pair was
combined with three different C3s, such that C3 would be the least similar to C1-C2.
Based on previous studies, the control on C3 was based on place of articulation
(see McCarthy 1979, 1981, 1986; Frisch et al. 2004 among others for OCP-Place
violations in Semitic languages). C3s were selected in the following way:
a. If C1 and C2 did not share a major place of articulation (labial, coronal or dorsal),
C3 was in the third place of articulation. Note that C3 could not be b or p since it is
a spirant position. For example, C3s for the pair b (labial) and d (coronal) were
dorsals.
b. If C1 and C2 shared a major place of articulation, C3 was chosen in the following
way: if C1 and C2 were labials, the chosen C3s were dorsals; if C1 and C2 were
coronals, the chosen C3s were labials; if C1 and C2 were dorsals, the chosen C3s
were coronals.
c. When this strategy did not result in enough suitable candidates for C3 (see next
paragraph for reasons for candidate elimination), I chose a different place of
articulation, such that C2 and C3 would not be at the same place.
No stimuli were part of the Hebrew verbal lexicon. Moreover, they did not share
stem consonants with existing verbs in different classes (binyanim), for example nonceverbs such as baʁaʃ (shared stem consonants with hivʁiʃ 'to brush X') and bakaʃ (shared
stem consonants with bikeʃ 'to request') were not included in the experiment. Stimuli
that obeyed these conditions but formed existing words in Hebrew (e.g. paɡaz 'shell
(projectile)') were also excluded.
In cases in which three different options for C3 could not be found, one form
was repeated in order to obtain three stimuli for each C1-C2 pair. Overall, the stimuli
contained 147 tested consonant pairs C1-C2, forming 331 different nonce-verbs, and
31
441 stimuli including repetitions. In order to make the experiment shorter, each
participant was exposed to a random list of 49 stimuli (in random order as well), in
which each tested pair of segments was presented at most once.
The nonce-verbs were inserted into frame sentences in the template of male
proper name + verb + an animal. For example, xen baʦaɡ et ha-tanin 'Chen baʦaɡ-ed
(nonce-verb) the crocodile'. Inserting the nonce-verbs into frames had two main
reasons: first, the template of CaCaC is used both for verbs and nouns, for example
paɡaz 'shell (projectile)', zamaʁ 'singer'. Combining the nonce-words in sentences
ensures that the participants would refer to the nonce-word as a verb and not as a noun.
Second, the sentences were intended to make the experiment more interesting than
presenting verbs in isolation, and I hoped that it would make participants more attentive
to the task. The fixed template was aimed at controlling for semantic effects on the
judgments. The stimuli were recorded by a 26-year-old female native speaker of
Hebrew (the author), a full stimuli list appears in Appendix G.
7.2.3 Procedure
The experiment was conducted online via the Qualtrics website (www.qualtrics.com).
Each participant heard a random list of 49 sentences, and was asked to rate the wordlikelihood of each nonce-verb. The ratings were on a scale of 1 to 7, which was defined
as follows (here translated to English), based on Frisch and Zawaydeh (2001):
(14) The rating scale
1 – No. The verb sounds terrible; it cannot be a valid verb in Hebrew.
3 – Not likely. The verb sounds strange; I doubt it can be a valid verb in Hebrew.
5 – Maybe. The verb sounds a bit strange, but it can possibly be a valid verb in Hebrew.
7 – Yes. The verb sounds good; it can be a valid verb in Hebrew.
2, 4 and 6 are found between these guidelines.
32
The instructions were shown after each question. Since I hypothesized that
word-likelihood would be gradient rather than dichotomous, I chose to use a scale and
not a yes\no decision in order to allow participants to express small differences in
judgments (see Kawahara 2011 for a different approach).
As in the lexical decision experiment, this experiment also focuses on auditory
similarity and not visual similarity. Therefore, all the sentences were presented
auditorily, in order to focus on the auditory channel, and avoid orthographic or visual
influence. The experiment took approximately ten minutes.
7.2.4 Results
The results were calculated as follows: first, a pre-analysis was done in order to check
whether the effect of C3 was neutralized. Second, the correlation between the results
and the previous parts of the study (the lexicon analysis and the similarity scale) was
tested.
7.2.4.1 Pre-analysis: The Effect of C3
Since the study focuses on C1 and C2, C3 was carefully chosen in order to reduce its
influence on the results. Hence, before analyzing the effects of C1 and C2, I checked
whether C3 in each triplet of stimuli influenced the participants' judgments. For
example, I checked whether the ratings for bafat, bafad and bafan were significantly
different.
In order to test this possibility, a Kruskal-Wallis test was run on the 119 cases in
which different C3s were combined to the tested C1-C2 pairs. Out of the 119 cases, only
19 were found significant, namely there were 19 triplets in which combining different
C3s affected the results (see Appendix H for a full list). Note that statistically, even if
the assumption that C3 did not affect the results was true, we would expect an error of
5%, namely six cases in which C3 affected the results.
33
Many of the anomalous stimuli are nonce-verbs that are very similar to real
Hebrew verb, usually different only in the voicing of one consonant. For example, the
nonce-verb baʁas is similar to the Hebrew verb paʁas 'to spread/slice', the nonce-verb
baʦax is similar to paʦax 'to start' and the nonce-verb padak is similar to badak 'to
check'. This finding implies another type of similarity that affects judgments: Not only
similarity between consonants is relevant to grammar, but also similarity on a higher
level, between words. This finding is compatible with Frisch and Zawaydeh (2001)
findings in Arabic.
Next, I converted the average rating for each triplet into a single value. The 19
cases in which a significant difference was found inside the triplets were eliminated
from the calculations.
7.2.4.2 Observations
A few interesting observations can be made by looking at the bottom of the results list,
namely on the items that were rated the lowest on the word-likelihood scale (mean
ratings for all tested consonant pairs appear in Appendix I). Table (15) presents the
consonant pairs that received the ten lowest ratings, their similarity value on the
similarity scale and their number of occurrences in the lexicon.
34
(15) the ten lowest ranked consonant pairs (in brackets: ranking on the similarity list,
out of 66)24
Pair
Rating Similarity
Occurrences
1 s-s
2.05
1
0
2 ʦ-s
2.21
0.414 (7)
0
3 k-ɡ
2.27
0.462 (5)
0
4 ʃ-ʃ
2.29
1
0
5 z-z
2.51
1
0
6 t-d
2.53
0.5 (3)
0
7 t-ʦ
2.60
0.7 (1)
6 (2 verbs)
8 b-v
2.65
0.313 (10)
0
8 ʦ-ʁ
2.65
0 (66)
51
9 s-z
2.66
0.52 (2)
0
9 k-k
2.66
1
1 (kikev)
10 ɡ-ɡ
2.67
1
0
A few observations can be made. All the items (except one) are rated among the
ten highest similar pairs on the similarity scale, share the major place feature, and have
zero, or almost zero, occurrences in the lexicon. The correlation between the parameters
is salient. The only pair among the most similar tested pairs that does not appear among
the ten lowest pairs in the experiment is ʃ-s (similarity value: 0.5), with a mean result
of 3.15 (placed 22).
The only pair that does not follow this generalization is ʦ-ʁ, which has a
similarity value of 0 and 51 occurrences in the lexicon. An explanation for this
exception can be made by examining the chosen C3s for this pair: the three items were
ʦaʁaʃ, ʦaʁas and ʦaʁaz, all C3s are stridents as C1 ʦ. A key condition in this experiment
was that all stimuli would be nonce-verbs, and since the pair ʦ-ʁ has a large number of
occurrences in the lexicon, there are not many options left for nonce-verb stimuli. Along
24
Identical consonants had similarity value of 1, and were not considered in the ranking.
35
the lines of the scheme presented in §7.2.2, all the chosen C3s were stridents. Therefore,
the low ratings for this pair could have been influenced by the similarity between C1
and C3, and not only by the relations between C1 and C2.
Nevertheless, not all the most similar pairs nor pairs with zero occurrences in
the lexicon appear at the top of the experiment results. Looking at the other results
reveals that all the pairs with identical C1-C2 are rated among the lowest 20. For
example, l-l, m-m and n-n are rated 11th, 12th and 13th respectively, and x-x is rated
19th.25 Note that these are exactly the identical pairs that do have occurrences in the
lexicon (together with k-k), and x-x had the highest number of occurrences, 15, affected
by historical reasons (see §6.2.2 for discussion). This finding can show the correlation
between the lexicon and the experiment, but not to point to the source of the influence.
It could be that the small-but-not-zero occurrences in the lexicon affect the speakers'
phonological system, or that these identical pairs show (for some reasons) fewer
restrictions, and therefore are more flexible both in the lexicon and in the speakers'
judgments. Nonetheless, the differences between the pairs of identical C 1-C2 are small,
and it can be concluded that all of them show co-occurrence restrictions in the speakers'
judgments.
Next, I examined the other pairs that had zero occurrences in the lexicon. Table
(16) summarizes the comparison.
25
The other pairs with identical C1-C2 were eliminated from the results according to the pre-analysis of
the effect of C3 (§7.2.4.1).
36
(16) Other pairs with zero occurrences in the lexicon (in brackets: the rank on the
relevant scale)26
Pair
Rating
p-f
2.81 (16)
b-f
2.93 (18)
d-z
3.00 (20)
b-m
3.02 (21)
p-v
3.21 (24)
n-l
3.42 (27)
m-f
3.43 (28)
m-v
3.55 (34)
ʁ-l
3.70 (38)
It can be seen that not all these pairs were rated low in the judgment task. There
are pairs that appear among the second or third group of ten, but also pairs that rated 34
and 38. Note that m-f and m-v, that rated low on the similarity scale (0.063 (51)), do not
show strong co-occurrence restrictions in the judgments task, as the pair ʁ-l, in which
C1 and C2 do not share a place of articulation. Hence, in these cases the similarity factors
are stronger than the occurrences in the lexicon.
7.2.4.3 Comparisons to the Scales
Next, a statistical analysis was done in order to examine the correlation between the
experiment results on the one hand, and the lexicon analysis and the similarity scale on
the other hand.
Comparison to the similarity scale: An ordered logistic regression model was
built in order to test the influence of the similarity factor on the results. This factor is
assumed to be negative, since the greater the similarity between two consonant is, the
participants are assumed to give lower ratings to the nonce-verbs. Indeed, the similarity
26
Not including the pairs that were eliminated due to spirantization (b-p, f-v) or during the pre-analysis
of the effect of C3.
37
factor is negative and its influence is significant (Estimate = -1.79, SD = 0.1, p <
0.0001). This result shows that the more similar the consonants are (their similarity
value is closer to 1), the lower the ratings the participants will give to the wordlikelihood of the nonce-verb.
Comparison to the lexical analysis: An ordered logistic regression model was
built in order to test the influence of the frequency factor on the results. This factor was
assumed to be positive, since the more frequent the consonant pair in the lexicon is, the
participants are assumed to give higher ratings to the nonce-verb. Indeed, the frequency
factor is positive and its influence is significant (Estimate = 0.04, SD = 0.004, p <
0.0001). This result shows that nonce-verb containing frequent pairs of consonants
receive higher ratings of word-likelihood.
These results show a strong correlation between the word-likelihood ratings and
the similarity scale and with the lexical analysis. Since a correlation was found between
the lexical analysis and the similarity scale as well, it is not surprising that the
participants' ratings correlate with both of them.
38
CHAPTER 8: GENERAL DISCUSSION
The study examines the co-occurrence restrictions in the Hebrew verbal system,
focusing on the contribution of similarity between consonants to these restrictions. The
phenomenon was tested on two levels: lexical analysis and psycholinguistic
experiments. The results suggest that there are co-occurrence restrictions on stem
consonants C1 and C2, both in the lexicon and in the speakers' phonological system. The
similarity factor was found to be significant in all the tested cases.
The similarity model used in this study is based on Frisch et al.'s (2004) model,
originally proposed for Arabic. The model is built with a phonological orientation: it is
based on phonological natural classes and phonological features. Note that the model
itself is not unique for linguistic similarity and can be adapted to similarity in any
domain; it is the phonological features that make the similarity model language
oriented. Since a correlation was found between the co-occurrence restrictions and the
similarity model, the study supports the idea that phonological features can constitute a
proper base for similarity calculations. However, the fact that there are articulatorybased similarity effects does not mean that acoustic factors do not play a role as well
(see Mielke 2009). It would be interesting to examine the interaction between acoustic
factors and phonological factors, and this is a window for further studies in the field.
Previous studies on Semitic languages (Greenberg 1950; McCarthy 1981, 1986;
Frisch et al. 2004 among others) demonstrated the important role of OCP-Place in cooccurrence restrictions in those languages. The current study strengthens this claim;
both the lexical analysis and the experiments suggest that consonants that share the
major place feature are less likely to co-occur. However, the results and the correlation
with the similarity scale show that not only place feature has a role in co-occurrence
restrictions, and correlations were found also between the similarity scale and
39
occurrences or judgments of pairs that do not share place of articulation. It is likely that
the major place feature has a great weight in similarity, inter alia due to its high position
in the feature geometry hierarchy (Clements 1985, Sagey 1986, Clements and Hume
1995, and see also Kaisse 1988 and Padgett 1995). Frisch et al.'s model does not suggest
which phonological features are more important for similarity, and the current findings
open a window for further research in the field.
The results of the lexical analysis and the experiment suggest a correlation
between the co-occurrence restrictions and the similarity scale. These correlations are
statistical, and do not entail causal influence of similarity on the restrictions.
The lexical analysis shows correlation to the similarity scale. Nonetheless, it is
plausible to assume that other factors have influenced the lexicon as well; some of them
are historical influences that do not have transparent evidence nowadays. For example,
the 15 occurrences of x-x in the lexicon: historically, the origin of x is double, one is a
result of historical ħ (which is assumed to have been pronounced farther backward than
the synchronic x) and the other is a spirantized k. Indeed, all the 15 cases of cooccurrences are results of the historical reasons. The naïve Hebrew speakers may not
know this detail, but the frequency of x in their lexicon and the cases of verbs with x in
C1 and C2 are likely to affect their phonological system. Thus, the correlation between
the lexical analysis and the similarity scale may suggest that similarity is one of the
factors that influence the lexicon. However, it is not the only one.
The speakers' word-likelihood judgments in the experiment were correlated both
with the similarity scale and the lexical analysis results. It is not surprising that the
experiment's results are correlated with both of them, because there is also a correlation
between the similarity scale and the lexical analysis results. Therefore, the experiment
40
cannot suggest whether the influence of similarity is direct, or indirect through the
lexical influences on the grammatical system (figure 17).
(17)
lexicon similarity → grammatical system
or:
similarity → lexicon → grammatical system
41
CHAPTER 9: CONCLUDING REMARKS
The study shows that there are co-occurrence restrictions both in the Hebrew lexicon
and in the grammatical system of the speakers. These restrictions have a strong
correlation with similarity between consonants, where the tendency is to avoid similar,
close consonants. The results suggest that similarity affects the speakers' wordlikelihood judgments, but they cannot tell whether the effect is direct or indirect through
the lexicon.
The study opens the door to further research on similarity effects in Hebrew and
in other languages. First, the current study examines the issue of similarity from a
phonological point of view, and successfully shows the relevance of phonological
properties to the subject. Future studies should examine the influence of each
phonological feature individually, and examine which features are more important to
similarity. Under the observed importance of the place of articulation, and previous
studies on OCP-Place in Semitic languages (McCarthy 1979, 1981, 1986; Frisch et al.
2004 among others), there is a reason to assume that major place features have a large
role in similarity.
Second, the study took into account phonological features that are mostly based
on articulatory factors, but not acoustic parameters per-se. However, it has been claimed
that acoustic factors play a significant role in similarity as well (Kawahara 2007, Mielke
2009). Models that combine acoustic parameters (exclusively or with articulatory
parameters) and test their predictions regarding similarity in Hebrew should be taken
into consideration in further studies.
Third, the lexical analysis does not show statistical differences between orders
of consonants in the pairs (e.g. k-d vs. d-k), but a closer look reveals large differences
in some pairs (e.g. k-d: 34 occurrences, d-k: 18 occurrences). It would be interesting to
42
examine possible causes to asymmetry in similarity pairs. Previous studies (Johnson
2012 for example) suggest that similarity is not symmetric, such that it is not necessary
that k is similar to d in the same degree that d is similar to k. Frisch et al.'s (2004) model
calculates similarity based on shared and non-shared natural classes, and thus cannot
take symmetry into account. However, it is also plausible to assume that other factors
cause these differences, such sonority or place of articulation.
Furthermore, the current study does not show differences between the two tested
verb classes (binyanim) kal and pi'el: They both showed the same tendency to avoid
similar consonant pairs in the lexicon. However, there is a solid basis to assume that
there are differences in co-occurrence restrictions between them. For one, the proximity
between C1 and C2 is different between the templates throughout the paradigms: in pi'el
they are separated with a vowel through the different tenses and persons, while in kal
there are forms in which they are adjacent (e.g. future tense: jixtov 'he will write'). Since
previous studies suggest that proximity plays a role in co-occurrence restrictions (Rose
2000 among others), and under the assumption that speakers have access to the
paradigms and not only to the base forms, differences in restrictions are expected. Since
the lexicon did not show significant differences, examination of this issue from a
psycholinguistic point of view in needed.
In addition, the current study focuses only on some verb classes and does not
deal with nouns at all. It would be interesting to compare co-occurrence restrictions
between nouns and verbs, and between two types of Hebrew nouns: Semitic nouns that
have similar structure to verbs, with stems and templates (e.g. ʃmiʁa 'saving', template:
CCiCa) and mono-morphemic, non-Semitic nouns (e.g. ʃulxan 'table'). It will also be
interesting to expand the study to other verb classes (binyanim), and look for differences
among them.
43
Finally, the study shows a correlation between the lexical analysis and the
speakers' judgments. It would be interesting to further examine the influence of the
lexicon on one's grammatical knowledge, and investigate the tension between universal
constraints and language specific grammar. Since the current study found correlation
between the similarity scale and the lexicon analysis, it cannot determine the source of
the influence; a language in which there is no correlation between the lexicon and the
universal constraints could shed more light on the influence of the lexicon.
44
APPENDIX
Appendix A: Natural classes of Hebrew consonants
j
ʁ
f
v
t
d
f
v
d
s
z
ʦ
ʃ
s
z
ʦ
ʃ
[-son, COR, +strident, +ant]
s
z
ʦ
[-son, COR, +strident, +ant, +cont]
s
z
[-son, COR, +strident, +ant, +cont, +voice]
z
[-son, COR, +strident, +ant, +cont, -voice]
s
[-son, COR, +strident, +ant, -cont]
ʦ
[-son, COR, +strident, +ant, -cont, -voice]
ʦ
[-son, COR, +strident, +ant, -voice]
s
[-son, COR, +strident, -ant]
ʃ
[-son, COR, +strident, -ant, +cont]
ʃ
[-son, COR, +strident, -ant, +cont, -voice]
ʃ
[-son, COR, +strident, +cont]
s
z
[-son, COR, +strident, +cont, -voice]
s
ʃ
[-son, COR, +strident, -voice]
s
ʦ
[+son]
m
n
l
[+son, LAB]
m
[+son, LAB, -cont]
m
[+son, COR]
n
l
j
[+son, COR, +ant]
n
l
[+son, COR, +ant, +cont]
l
[+son, COR, +ant, -cont]
n
[+son, COR, -ant]
j
[+son, COR, -ant, +cont]
j
[+son, COR, +cont]
l
[+son, COR, -cont]
n
[+son, DOR]
ʁ
[+son, DOR, +cont]
ʁ
[+son, +cont]
l
j
[+son, -cont]
m
n
[-son]
p
b
[-son, LAB]
p
b
[-son, LAB, +cont]
f
v
[-son, LAB, +cont, +voice]
v
[-son, LAB, +cont, -voice]
f
[-son, LAB, -cont]
p
[-son, LAB, -cont, +voice]
b
[-son, LAB, -cont, -voice]
p
[-son, LAB, +voice]
b
v
[-son, LAB, -voice]
p
f
[-son, COR]
t
[-son, COR, +strident]
j
ʁ
b
ʦ
45
ʃ
ʃ
s
z
ʦ
ʃ
k
ɡ
x
[-son, COR, +ant]
t
d
s
[-son, COR, +ant, -cont]
t
d
ʦ
[-son, COR, +ant, -cont, +voice]
d
[-son, COR, +ant, -cont, -voice]
t
ʦ
[-son, COR, +ant, +voice]
d
z
[-son, COR, +ant, -voice]
t
s
ʦ
[-son, COR, -voice]
t
s
ʦ
[-son, DOR]
k
ɡ
x
[-son, DOR, +cont]
x
[-son, DOR, +cont, -voice]
x
[-son, DOR, -cont]
k
[-son, DOR, -cont, +voice]
ɡ
[-son, DOR, -cont, -voice]
k
[-son, DOR, -voice]
k
x
[-son, +cont]
f
v
[-son, +cont, +voice]
v
z
[-son, +cont, -voice]
f
[-son, -cont]
z
ʦ
ʃ
ɡ
ʃ
x
ʦ
k
ɡ
ʃ
k
x
ʦ
ʃ
n
l
j
ʦ
n
l
ʃ
l
j
x
ʁ
d
ʦ
n
k
ɡ
s
z
s
ʃ
x
p
b
t
d
[-son, -cont, +voice]
b
d
ɡ
[-son, -cont, -voice]
p
t
ʦ
k
[-son, +voice]
b
v
d
z
ɡ
[-son, -voice]
p
f
t
s
ʦ
[LAB]
p
b
m
f
v
[LAB, -cont]
p
b
m
[COR]
t
d
s
z
[COR, +ant]
t
d
s
z
[COR, +ant, +cont]
s
z
l
[COR, +ant, -cont]
t
d
ʦ
n
[COR, -ant]
ʃ
j
[COR, -ant, +cont]
ʃ
j
[COR, +cont]
s
z
ʃ
l
[DOR]
k
ɡ
x
ʁ
[DOR, +cont]
x
ʁ
[+cont]
f
v
s
z
[-cont]
p
b
m
t
46
j
Appendix B: The similarity scale (based on Frisch et al.'s 2004 model)
bl
0.000
bx
0.048
tl
0.087
kʦ
0.200
tʦ
0.700
bʁ
0.000
xl
0.048
xt
0.087
nt
0.200
bb
1.000
bj
0.000
xj
0.048
dl
0.091
pm
0.200
dd
1.000
dʁ
0.000
bn
0.050
tf
0.091
ʁl
0.200
ff
1.000
ɡl
0.000
fj
0.050
dv
0.095
ʁj
0.200
ɡɡ
1.000
ɡj
0.000
lf
0.050
px
0.100
xʁ
0.200
kk
1.000
kl
0.000
lv
0.050
sj
0.103
ʃx
0.208
ll
1.000
kj
0.000
pn
0.050
sv
0.107
ʃj
0.208
mm
1.000
mʃ
0.000
tm
0.050
bʦ
0.107
nd
0.211
nn
1.000
mz
0.000
vj
0.050
ʃd
0.107
nm
0.214
pp
1.000
nf
0.000
dm
0.053
kf
0.111
ʃf
0.217
ʁʁ
1.000
nv
0.000
nk
0.053
ɡʦ
0.115
zl
0.217
ss
1.000
pl
0.000
kv
0.053
ʃl
0.115
ʃʦ
0.226
ʃʃ
1.000
pʁ
0.000
ɡf
0.056
ɡv
0.118
zv
0.227
tt
1.000
pj
0.000
nɡ
0.056
ʃv
0.120
ʦz
0.233
ʦʦ
1.000
sm
0.000
ml
0.059
xz
0.120
pt
0.250
vv
1.000
tʁ
0.000
mj
0.059
zj
0.120
kt
0.263
xx
1.000
ʦʁ
0.000
kʁ
0.063
zf
0.125
bd
0.263
jj
1.000
xm
0.000
mf
0.063
bt
0.136
ɡd
0.294
zz
1.000
xn
0.000
mv
0.063
pd
0.143
st
0.296
bs
0.032
ʁf
0.063
kd
0.150
ʃz
0.296
ʦj
0.033
ʁn
0.063
tɡ
0.150
dz
0.304
sɡ
0.034
km
0.063
ʦn
0.154
bv
0.313
ʦv
0.034
ʁv
0.063
bf
0.167
nl
0.313
bʃ
0.036
ɡm
0.067
bk
0.167
pf
0.313
nʃ
0.037
ɡʁ
0.067
nj
0.167
pk
0.313
pz
0.037
ps
0.067
pv
0.167
xf
0.313
sʁ
0.037
sn
0.069
xv
0.167
xk
0.313
ʃɡ
0.038
ʦl
0.069
sd
0.172
bɡ
0.333
ʦm
0.038
ʦx
0.069
ɡx
0.176
ʦd
0.375
zk
0.038
ks
0.069
pɡ
0.176
ʦs
0.414
ʃʁ
0.042
ʦf
0.071
ʃt
0.185
fv
0.429
tj
0.042
pʃ
0.074
sl
0.185
kɡ
0.462
tv
0.043
bz
0.077
sx
0.185
bp
0.467
dx
0.043
mʁ
0.077
pʦ
0.192
lj
0.467
dj
0.043
kʃ
0.077
sf
0.192
ʃs
0.500
zʁ
0.043
nz
0.080
tz
0.192
td
0.500
df
0.045
ɡz
0.083
bm
0.200
sz
0.520
47
Appendix C: Sample derivations
kal (3rd ms. sg.)
pi'el (3rd ms. sg.)
Past
Present
Future
Past
Present
Future
Regular Verbs
ʃamaʁ
ʃomeʁ
jiʃmoʁ
'save'
ʃimeʁ meʃameʁ
jeʃameʁ
'preserve'
C1: spirantization
katav
kotev
jixtov
'write'
kitev
mexatev
jexatev
'subscribe'
C2: spirantization
safaʁ
sofeʁ
jispoʁ
'count'
sipeʁ
mesapeʁ
jesapeʁ
'tell'
Appendix D: Lexical analysis results
kal
pi'el
kal and pi'el
bb
0
bb
0
sum
0
bb
0
bb
0
sum
0
bb
0
bb
0
sum
0
bd
8
db
4
sum
12
bd
4
db
9
sum
13
bd
12
db
13
sum
25
bf
0
fb
0
sum
0
bf
0
fb
0
sum
0
bf
0
fb
0
sum
0
bɡ
4
ɡb
4
sum
8
bɡ
0
ɡb
18
sum
18
bɡ
4
ɡb
22
sum
26
bk
2
kb
4
sum
6
bk
3
kb
21
sum
24
bk
5
kb
25
sum
30
bl
12
lb
4
sum
16
bl
4
lb
18
sum
22
bl
16
lb
22
sum
38
bm
0
mb
0
sum
0
bm
0
mb
0
sum
0
bm
0
mb
0
sum
0
bn
0
nb
3
sum
3
bn
0
nb
9
sum
9
bn
0
nb
12
sum
12
bp
0
pb
0
sum
0
bp
0
pb
0
sum
0
bp
0
pb
0
sum
0
bʁ
16
ʁb
5
sum
21
bʁ
6
ʁb
15
sum
21
bʁ
22
ʁb
20
sum
42
bs
6
sb
5
sum
11
bs
5
sb
15
sum
20
bs
11
sb
20
sum
31
bʃ
2
ʃb
8
sum
10
bʃ
1
ʃb
15
sum
16
bʃ
3
ʃb
23
sum
26
bt
6
tb
2
sum
8
bt
5
tb
9
sum
14
bt
11
tb
11
sum
22
bʦ
6
ʦb
3
sum
9
bʦ
2
ʦb
6
sum
8
bʦ
8
ʦb
9
sum
17
bv
0
vb
0
sum
0
bv
0
vb
0
sum
0
bv
0
vb
0
sum
0
bx
8
xb
11
sum
19
bx
5
xb
36
sum
41
bx
13
xb
47
sum
60
bz
6
zb
3
sum
9
bz
0
zb
9
sum
9
bz
6
zb
12
sum
18
dd
0
dd
0
sum
0
dd
0
dd
0
sum
0
dd
0
dd
0
sum
0
df
6
fd
0
sum
6
df
0
fd
2
sum
2
df
6
fd
2
sum
8
dɡ
6
ɡd
15
sum
21
dɡ
12
ɡd
15
sum
27
dɡ
18
ɡd
30
sum
48
dk
6
kd
19
sum
25
dk
12
kd
15
sum
27
dk
18
kd
34
sum
52
dl
15
ld
0
sum
15
dl
9
ld
0
sum
9
dl
24
ld
0
sum
24
dm
6
md
3
sum
9
dm
6
md
9
sum
15
dm
12
md
12
sum
24
dn
0
nd
10
sum
10
dn
3
nd
9
sum
12
dn
3
nd
19
sum
22
dp
3
pd
0
sum
3
dp
6
pd
1
sum
7
dp
9
pd
1
sum
10
dʁ
12
ʁd
9
sum
21
dʁ
6
ʁd
6
sum
12
dʁ
18
ʁd
15
sum
33
ds
0
sd
9
sum
9
ds
3
sd
9
sum
12
ds
3
sd
18
sum
21
dʃ
3
ʃd
9
sum
12
dʃ
3
ʃd
9
sum
12
dʃ
6
ʃd
18
sum
24
48
kal
pi'el
kal and pi'el
dt
0
td
0
sum
0
dt
0
td
0
sum
0
dt
0
td
0
sum
0
dʦ
0
ʦd
3
sum
3
dʦ
0
ʦd
6
sum
6
dʦ
0
ʦd
9
sum
9
dv
8
vd
4
sum
12
dv
0
vd
8
sum
8
dv
8
vd
12
sum
20
dx
15
xd
11
sum
26
dx
9
xd
18
sum
27
dx
24
xd
29
sum
53
dz
0
zd
0
sum
0
dz
0
zd
0
sum
0
dz
0
zd
0
sum
0
ff
0
ff
0
sum
0
ff
0
ff
0
sum
0
ff
0
ff
0
sum
0
fɡ
4
ɡf
0
sum
4
fɡ
6
ɡf
0
sum
6
fɡ
10
ɡf
0
sum
10
fk
7
kf
24
sum
31
fk
10
kf
0
sum
10
fk
17
kf
24
sum
41
fl
4
lf
6
sum
10
fl
14
lf
0
sum
14
fl
18
lf
6
sum
24
fm
0
mf
0
sum
0
fm
0
mf
0
sum
0
fm
0
mf
0
sum
0
fn
1
nf
6
sum
7
fn
6
nf
0
sum
6
fn
7
nf
6
sum
13
fp
0
pf
0
sum
0
fp
0
pf
0
sum
0
fp
0
pf
0
sum
0
fʁ
11
ʁf
12
sum
23
fʁ
4
ʁf
0
sum
4
fʁ
15
ʁf
12
sum
27
fs
6
sf
16
sum
22
fs
8
sf
0
sum
8
fs
14
sf
16
sum
30
fʃ
4
ʃf
12
sum
16
fʃ
6
ʃf
0
sum
6
fʃ
10
ʃf
12
sum
22
ft
8
tf
22
sum
30
ft
12
tf
0
sum
12
ft
20
tf
22
sum
42
fʦ
3
ʦf
8
sum
11
fʦ
4
ʦf
0
sum
4
fʦ
7
ʦf
8
sum
15
fv
0
vf
0
sum
0
fv
0
vf
0
sum
0
fv
0
vf
0
sum
0
fx
6
xf
14
sum
20
fx
12
xf
0
sum
12
fx
18
xf
14
sum
32
fz
4
zf
2
sum
6
fz
6
zf
0
sum
6
fz
10
zf
2
sum
12
ɡɡ
0
ɡɡ
0
sum
0
ɡɡ
0
ɡɡ
0
sum
0
ɡɡ
0
ɡɡ
0
sum
0
ɡk
0
kɡ
0
sum
0
ɡk
0
kɡ
0
sum
0
ɡk
0
kɡ
0
sum
0
ɡl
18
lɡ
6
sum
24
ɡl
9
lɡ
0
sum
9
ɡl
27
lɡ
6
sum
33
ɡm
15
mɡ
6
sum
21
ɡm
15
mɡ
9
sum
24
ɡm
30
mɡ
15
sum
45
ɡn
12
nɡ
13
sum
25
ɡn
6
nɡ
18
sum
24
ɡn
18
nɡ
31
sum
49
ɡp
0
pɡ
8
sum
8
ɡp
12
pɡ
3
sum
15
ɡp
12
pɡ
11
sum
23
ɡʁ
27
ʁɡ
18
sum
45
ɡʁ
15
ʁɡ
18
sum
33
ɡʁ
42
ʁɡ
36
sum
78
ɡs
3
sɡ
9
sum
12
ɡs
0
sɡ
15
sum
15
ɡs
3
sɡ
24
sum
27
ɡʃ
9
ʃɡ
12
sum
21
ɡʃ
9
ʃɡ
15
sum
24
ɡʃ
18
ʃɡ
27
sum
45
ɡt
0
tɡ
0
sum
0
ɡt
3
tɡ
6
sum
9
ɡt
3
tɡ
6
sum
9
ɡʦ
0
ʦɡ
0
sum
0
ɡʦ
3
ʦɡ
0
sum
3
ɡʦ
3
ʦɡ
0
sum
3
ɡv
8
vɡ
2
sum
10
ɡv
0
vɡ
0
sum
0
ɡv
8
vɡ
2
sum
10
ɡx
9
xɡ
6
sum
15
ɡx
6
xɡ
3
sum
9
ɡx
15
xɡ
9
sum
24
ɡz
12
zɡ
0
sum
12
ɡz
6
zɡ
3
sum
9
ɡz
18
zɡ
3
sum
21
kk
0
kk
0
sum
0
kk
1
kk
0
sum
1
kk
1
kk
0
sum
1
kl
17
lk
10
sum
27
kl
14
lk
12
sum
26
kl
31
lk
22
sum
53
km
26
mk
3
sum
29
km
12
mk
15
sum
27
km
38
mk
18
sum
56
kn
10
nk
21
sum
31
kn
9
nk
33
sum
42
kn
19
nk
54
sum
73
kp
6
pk
12
sum
18
kp
12
pk
5
sum
17
kp
18
pk
17
sum
35
49
kal
pi'el
kal and pi'el
kʁ
34
ʁk
20
sum
54
kʁ
0
ʁk
33
sum
33
kʁ
34
ʁk
53
sum
87
ks
16
sk
16
sum
32
ks
6
sk
33
sum
39
ks
22
sk
49
sum
71
kʃ
25
ʃk
24
sum
49
kʃ
14
ʃk
33
sum
47
kʃ
39
ʃk
57
sum
96
kt
21
tk
10
sum
31
kt
26
tk
21
sum
47
kt
47
tk
31
sum
78
kʦ
12
ʦk
0
sum
12
kʦ
9
ʦk
0
sum
9
kʦ
21
ʦk
0
sum
21
kv
16
vk
1
sum
17
kv
0
vk
6
sum
6
kv
16
vk
7
sum
23
kx
8
xk
13
sum
21
kx
3
xk
14
sum
17
kx
11
xk
27
sum
38
kz
2
zk
13
sum
15
kz
4
zk
15
sum
19
kz
6
zk
28
sum
34
ll
0
ll
0
sum
0
ll
3
ll
0
sum
3
ll
3
ll
0
sum
3
lm
3
ml
18
sum
21
lm
6
ml
6
sum
12
lm
9
ml
24
sum
33
ln
0
nl
0
sum
0
ln
0
nl
0
sum
0
ln
0
nl
0
sum
0
lp
3
pl
8
sum
11
lp
6
pl
7
sum
13
lp
9
pl
15
sum
24
lʁ
0
ʁl
0
sum
0
lʁ
0
ʁl
0
sum
0
lʁ
0
ʁl
0
sum
0
ls
0
sl
12
sum
12
ls
0
sl
21
sum
21
ls
0
sl
33
sum
33
lʃ
3
ʃl
24
sum
27
lʃ
3
ʃl
21
sum
24
lʃ
6
ʃl
45
sum
51
lt
9
tl
12
sum
21
lt
9
tl
18
sum
27
lt
18
tl
30
sum
48
lʦ
3
ʦl
18
sum
21
lʦ
0
ʦl
15
sum
15
lʦ
3
ʦl
33
sum
36
lv
8
vl
6
sum
14
lv
0
vl
8
sum
8
lv
8
vl
14
sum
22
lx
17
xl
28
sum
45
lx
6
xl
25
sum
31
lx
23
xl
53
sum
76
lz
0
zl
12
sum
12
lz
0
zl
6
sum
6
lz
0
zl
18
sum
18
mm
0
mm
0
sum
0
mm
6
mm
0
sum
6
mm
6
mm
0
sum
6
mn
0
nm
0
sum
0
mn
3
nm
15
sum
18
mn
3
nm
15
sum
18
mp
0
pm
0
sum
0
mp
0
pm
0
sum
0
mp
0
pm
0
sum
0
mʁ
18
ʁm
12
sum
30
mʁ
12
ʁm
6
sum
18
mʁ
30
ʁm
18
sum
48
ms
12
sm
12
sum
24
ms
9
sm
18
sum
27
ms
21
sm
30
sum
51
mʃ
12
ʃm
9
sum
21
mʃ
9
ʃm
18
sum
27
mʃ
21
ʃm
27
sum
48
mt
9
tm
21
sum
30
mt
18
tm
21
sum
39
mt
27
tm
42
sum
69
mʦ
9
ʦm
15
sum
24
mʦ
6
ʦm
18
sum
24
mʦ
15
ʦm
33
sum
48
mv
0
vm
0
sum
0
mv
0
vm
0
sum
0
mv
0
vm
0
sum
0
mx
18
xm
28
sum
46
mx
6
xm
24
sum
30
mx
24
xm
52
sum
76
mz
6
zm
6
sum
12
mz
6
zm
9
sum
15
mz
12
zm
15
sum
27
nn
0
nn
0
sum
0
nn
3
nn
0
sum
3
nn
3
nn
0
sum
3
np
1
pn
2
sum
3
np
15
pn
3
sum
18
np
16
pn
5
sum
21
nʁ
0
ʁn
3
sum
3
nʁ
0
ʁn
3
sum
3
nʁ
0
ʁn
6
sum
6
ns
7
sn
9
sum
16
ns
9
sn
15
sum
24
ns
16
sn
24
sum
40
nʃ
14
ʃn
9
sum
23
nʃ
21
ʃn
15
sum
36
nʃ
35
ʃn
24
sum
59
nt
18
tn
6
sum
24
nt
24
tn
6
sum
30
nt
42
tn
12
sum
54
nʦ
8
ʦn
12
sum
20
nʦ
6
ʦn
6
sum
12
nʦ
14
ʦn
18
sum
32
nv
8
vn
0
sum
8
nv
0
vn
0
sum
0
nv
8
vn
0
sum
8
50
kal
pi'el
kal and pi'el
nx
19
xn
23
sum
42
nx
18
xn
21
sum
39
nx
37
xn
44
sum
81
nz
7
zn
6
sum
13
nz
6
zn
9
sum
15
nz
13
zn
15
sum
28
pp
0
pp
0
sum
0
pp
0
pp
0
sum
0
pp
0
pp
0
sum
0
pʁ
22
ʁp
6
sum
28
pʁ
2
ʁp
15
sum
17
pʁ
24
ʁp
21
sum
45
ps
12
sp
8
sum
20
ps
4
sp
9
sum
13
ps
16
sp
17
sum
33
pʃ
8
ʃp
6
sum
14
pʃ
3
ʃp
9
sum
12
pʃ
11
ʃp
15
sum
26
pt
16
tp
11
sum
27
pt
6
tp
15
sum
21
pt
22
tp
26
sum
48
pʦ
6
ʦp
4
sum
10
pʦ
2
ʦp
9
sum
11
pʦ
8
ʦp
13
sum
21
pv
0
vp
0
sum
0
pv
0
vp
0
sum
0
pv
0
vp
0
sum
0
px
14
xp
13
sum
27
px
6
xp
24
sum
30
px
20
xp
37
sum
57
pz
8
zp
1
sum
9
pz
3
zp
6
sum
9
pz
11
zp
7
sum
18
ʁʁ
0
ʁʁ
0
sum
0
ʁʁ
0
ʁʁ
0
sum
0
ʁʁ
0
ʁʁ
0
sum
0
ʁs
6
sʁ
39
sum
45
ʁs
9
sʁ
3
sum
12
ʁs
15
sʁ
42
sum
57
ʁʃ
9
ʃʁ
9
sum
18
ʁʃ
15
ʃʁ
3
sum
18
ʁʃ
24
ʃʁ
12
sum
36
ʁt
24
tʁ
24
sum
48
ʁt
21
tʁ
18
sum
39
ʁt
45
tʁ
42
sum
87
ʁʦ
12
ʦʁ
21
sum
33
ʁʦ
9
ʦʁ
9
sum
18
ʁʦ
21
ʦʁ
30
sum
51
ʁv
10
vʁ
8
sum
18
ʁv
0
vʁ
12
sum
12
ʁv
10
vʁ
20
sum
30
ʁx
28
xʁ
44
sum
72
ʁx
15
xʁ
21
sum
36
ʁx
43
xʁ
65
sum
108
ʁz
6
zʁ
15
sum
21
ʁz
0
zʁ
15
sum
15
ʁz
6
zʁ
30
sum
36
ss
0
ss
0
sum
0
ss
0
ss
0
sum
0
ss
0
ss
0
sum
0
sʃ
0
ʃs
6
sum
6
sʃ
0
ʃs
3
sum
3
sʃ
0
ʃs
9
sum
9
st
18
ts
3
sum
21
st
15
ts
3
sum
18
st
33
ts
6
sum
39
sʦ
0
ʦs
0
sum
0
sʦ
0
ʦs
0
sum
0
sʦ
0
ʦs
0
sum
0
sv
10
vs
3
sum
13
sv
0
vs
10
sum
10
sv
10
vs
13
sum
23
sx
38
xs
26
sum
64
sx
9
xs
21
sum
30
sx
47
xs
47
sum
94
sz
0
zs
0
sum
0
sz
0
zs
0
sum
0
sz
0
zs
0
sum
0
ʃʃ
0
ʃʃ
0
sum
0
ʃʃ
0
ʃʃ
0
sum
0
ʃʃ
0
ʃʃ
0
sum
0
ʃt
21
tʃ
6
sum
27
ʃt
15
tʃ
0
sum
15
ʃt
36
tʃ
6
sum
42
ʃʦ
3
ʦʃ
0
sum
3
ʃʦ
0
ʦʃ
0
sum
0
ʃʦ
3
ʦʃ
0
sum
3
ʃv
16
vʃ
1
sum
17
ʃv
0
vʃ
2
sum
2
ʃv
16
vʃ
3
sum
19
ʃx
42
xʃ
23
sum
65
ʃx
18
xʃ
19
sum
37
ʃx
60
xʃ
42
sum
102
ʃz
6
zʃ
0
sum
6
ʃz
6
zʃ
0
sum
6
ʃz
12
zʃ
0
sum
12
tt
0
tt
0
sum
0
tt
0
tt
0
sum
0
tt
0
tt
0
sum
0
tʦ
0
ʦt
0
sum
0
tʦ
0
ʦt
6
sum
6
tʦ
0
ʦt
6
sum
6
tv
4
vt
3
sum
7
tv
0
vt
10
sum
10
tv
4
vt
13
sum
17
tx
26
xt
36
sum
62
tx
9
xt
40
sum
49
tx
35
xt
76
sum
111
tz
3
zt
0
sum
3
tz
3
zt
3
sum
6
tz
6
zt
3
sum
9
ʦʦ
0
ʦʦ
0
sum
0
ʦʦ
0
ʦʦ
0
sum
0
ʦʦ
0
ʦʦ
0
sum
0
ʦv
6
vʦ
3
sum
9
ʦv
0
vʦ
4
sum
4
ʦv
6
vʦ
7
sum
13
51
kal
pi'el
kal and pi'el
ʦx
12
xʦ
12
sum
24
ʦx
6
xʦ
12
sum
18
ʦx
18
xʦ
24
sum
42
ʦz
0
zʦ
0
sum
0
ʦz
0
zʦ
0
sum
0
ʦz
0
zʦ
0
sum
0
vv
0
vv
0
sum
0
vv
0
vv
0
sum
0
vv
0
vv
0
sum
0
vx
4
xv
14
sum
18
vx
10
xv
0
sum
10
vx
14
xv
14
sum
28
vz
3
zv
6
sum
9
vz
0
zv
0
sum
0
vz
3
zv
6
sum
9
xx
9
xx
0
sum
9
xx
6
xx
0
sum
6
xx
15
xx
0
sum
15
xz
13
zx
5
sum
18
xz
14
zx
0
sum
14
xz
27
zx
5
sum
32
zz
0
zz
0
sum
0
zz
0
zz
0
sum
0
zz
0
zz
0
sum
0
52
Appendix E: Frequencies in the lexicon
kal:
b
C1
76
3.29%
C2
57
2.44%
sum
133
2.86%
d
C1
84
3.64%
C2
102
4.36%
sum
186
4.01%
f
C1
58
2.51%
C2
128
5.48%
sum
186
4.01%
ɡ
C1
132
5.72%
C2
96
4.11%
sum
228
4.91%
k
C1
240
10.40%
C2
162
6.93%
sum
402
8.66%
l
C1
72
3.12%
C2
204
8.73%
sum
276
5.94%
m
C1
114
4.94%
C2
153
6.55%
sum
267
5.75%
n
C1
135
5.85%
C2
93
3.98%
sum
228
4.91%
p
C1
116
5.03%
C2
64
2.74%
sum
180
3.88%
ʁ
C1
180
7.80%
C2
300
12.84%
sum
480
10.34%
s
C1
201
8.71%
C2
108
4.62%
sum
309
6.65%
ʃ
C1
216
9.36%
C2
123
5.26%
sum
339
7.30%
t
C1
150
6.50%
C2
195
8.34%
sum
345
7.43%
ʦ
C1
102
4.42%
C2
78
3.34%
sum
180
3.88%
v
C1
38
1.65%
C2
114
4.88%
sum
152
3.27%
x
C1
324
14.04%
C2
282
12.07%
sum
606
13.05%
z
C1
69
2.99%
C2
78
3.34%
sum
147
3.17%
b
C1
35
1.72%
C2
180
8.85%
sum
215
5.29%
d
C1
78
3.83%
C2
117
5.75%
sum
195
4.79%
f
C1
90
4.42%
C2
0
0.00%
sum
90
2.21%
ɡ
C1
117
5.75%
C2
102
5.01%
sum
219
5.38%
k
C1
146
7.18%
C2
240
11.80%
sum
386
9.49%
l
C1
63
3.10%
C2
177
8.70%
sum
240
5.90%
m
C1
114
5.60%
C2
180
8.85%
sum
294
7.23%
n
C1
186
9.14%
C2
108
5.31%
sum
294
7.23%
p
C1
45
2.21%
C2
141
6.93%
sum
186
4.57%
ʁ
C1
165
8.11%
C2
126
6.19%
sum
291
7.15%
s
C1
162
7.96%
C2
90
4.42%
sum
252
6.19%
ʃ
C1
180
8.85%
C2
105
5.16%
sum
285
7.01%
t
C1
129
6.34%
C2
213
10.47%
sum
342
8.41%
ʦ
C1
81
3.98%
C2
57
2.80%
sum
138
3.39%
v
C1
70
3.44%
C2
0
0.00%
sum
70
1.72%
x
C1
298
14.65%
C2
144
7.08%
sum
442
10.87%
z
C1
75
3.69%
C2
54
2.65%
sum
129
3.17%
pi'el:
53
kal and pi'el:
b
C1
111
2.56%
C2
237
5.42%
sum
348
3.99%
d
C1
162
3.73%
C2
219
5.01%
sum
381
4.37%
f
C1
148
3.41%
C2
128
2.93%
sum
276
3.17%
ɡ
C1
249
5.74%
C2
198
4.53%
sum
447
5.13%
k
C1
386
8.89%
C2
402
9.20%
sum
788
9.04%
l
C1
135
3.11%
C2
381
8.72%
sum
516
5.92%
m
C1
228
5.25%
C2
333
7.62%
sum
561
6.44%
n
C1
321
7.39%
C2
201
4.60%
sum
522
5.99%
p
C1
161
3.71%
C2
205
4.69%
sum
366
4.20%
ʁ
C1
345
7.95%
C2
426
9.75%
sum
771
8.85%
s
C1
363
8.36%
C2
198
4.53%
sum
561
6.44%
ʃ
C1
396
9.12%
C2
228
5.22%
sum
624
7.16%
t
C1
279
6.43%
C2
408
9.33%
sum
687
7.89%
ʦ
C1
183
4.22%
C2
135
3.09%
sum
318
3.65%
v
C1
108
2.49%
C2
114
2.61%
sum
222
2.55%
x
C1
622
14.33%
C2
426
9.75%
sum
1048
12.03%
z
C1
144
3.32%
C2
132
3.02%
sum
276
3.17%
54
Appendix F: Lexical decision experiment: stimuli
Nonce-Verbs
0 shared features
1 shared feature
2 shared features
2 shared features
Identical C1-C2
(place)
(place and manner)
(place and voice)
ɡisem
disem
ʃizek
tiʦem
ʦiʦeɡ
ɡiʦem
liseɡ
sizek
tiʃem
ʃiʃeɡ
likem
liʦem
ziseɡ
ʦiʃem
ʦiʦem
miʃeɡ
ʦizek
zisem
tisem
ʃiʃem
ʃimek
zitem
ziʃeɡ
siʦeɡ
sisem
zikem
ziʦeɡ
ziʃem
ʃiʦeɡ
siseɡ
Real Verbs
bikeʃ
kibel
kipeʦ
pinek
ʃilem
biʃel
kibes
kiʃef
sibex
ʃilev
dileɡ
kidem
litef
sikem
ʃitef
ɡibeʃ
kilef
mizeɡ
silek
ʃitek
ɡilem
kimet
piked
sipek
tinef
kibed
kipel
pileɡ
ʃikem
ʦilem
Trial Session
Real Verbs
Nonce-Verbs
dibeʁ
bideɡ
limed
diɡev
sipeʁ
ɡidev
sixek
piɡet
xibek
tipeɡ
55
Appendix G: Word-likelihood judgment experiment: stimuli
All stimuli are in the form of: proper name + nonce-verb + the animal
Each participant got 49 random sentences, with a random verb from the bold triplet.
Yaron bafad\bafan\bafat et ha-ʃablul (snail)
Uri paxan\paxal\paxaʃ et ha-snai (squirrel)
Ron bakaz\bakat\bakas et ha-dolfin (dolphin)
Dror pafak\pafaɡ\pafaʁ et ha-atalef (bat)
Yonatan baɡaz\baɡat\baɡal et ha-ʃoʁ (bull)
Dan pamaɡ\pamak\pamax et ha-kaʁiʃ (shark)
Dror baxat\baxaʦ\baxas et ha-dvoʁa (bee)
Chen pavaɡ\pavaʁ\pavax et ha-taiʃ (male goat)
Tomer baʃak\baʃaɡ\baʃax et ha-tolaat (worm)
Ori pazak\pazax\pazaɡ et ha-janʃuf (owl)
Itamar bamax\bamaʁ\bamaɡ et ha-tiɡʁis (tiger)
Oz panak\panaʁ\panax et ha-zvuv (fly)
Elad banak\banax\banaʁ et ha-ɡdi (young goat)
Nitsan padak\padaʁ\padaɡ et ha-saknai (pelican)
Uri bavak\bavax\bavaɡ et ha-ʤiʁafa (giraffe)
Dor palaʁ\palaʁ\palak et ha-letaa (lizard)
Elad baʁal\baʁan\baʁas et ha-kof (monkey)
Omer pasaɡ\pasaɡ\pasaʁ et ha-ʦfaʁdea (frog)
Yossi balaʁ\balaɡ\balax et ha-tinʃemet (barn owl)
Doron paʃak\paʃaɡ\paʃak et ha-tuki (parrot)
Idan basak\basax\basaɡ et ha-axbaʁ (mouse)
Netanel paʦaɡ\paʦaɡ\paʦak et ha-akʁav (scorpion)
Chen baʦaɡ\baʦax\baʦaɡ et ha-tanin (crocodile)
Michael paʁan\paʁal\paʁan et ha-kof (monkey)
Eyal bazaɡ\bazax\bazax et ha-paʁoʃ (flea)
Yair pataɡ\pataɡ\pataɡ et ha-tolaat (worm)
Nadav badaɡ\badaɡ\badaɡ et ha-oʁev (crow)
Tomer ʁaval\ʁavan\ʁavat et ha-aʁje (lion)
Yoav bataɡ\bataɡ\bataɡ et ha-ʦipoʁ (bird)
Eran ʁafal\ʁafaz\ʁafan et ha-baʁvaz (duck)
Yuval dafax\dafaʁ\dafaʁ et ha-jona (pigeon)
Nir ʁalaf\ʁalav\ʁalam et ha-ʃual (fox)
Gal damaʁ\damak\damaɡ et ha-dov (bear)
Shay ʁanaf\ʁanam\ʁanav et ha-eɡel (calf)
Doron dadav\dadaf\dadam et ha-pinɡwin (penguin)
Saar ʁaʁam\ʁaʁaf\ʁaʁav et ha-xipuʃit (ladybug)
Michael dazam\dazav\dazaf et ha-ʦav (turtle)
Adam saɡaʃ\saɡaʦ\saɡat et ha-dvoʁa (bee)
Ran davaɡ\davax\davaɡ et ha-ʦav (turtle)
Yotam saxaz\saxaʃ\saxaʦ et ha-kipod (hedgehog)
Nadav daxam\daxav\daxav et ha-paʁpaʁ (butterfly)
Aviv sazam\sazav\sazaf et ha-kivsa (sheep)
Ram dalam\dalam\dalav et ha-taʁneɡol (rooster)
Boaz sadam\sadaf\sadav et ha-nameʁ (leopard)
Amit daʁaf\daʁaf\daʁav et ha-zeev (wolf)
Guy sasam\sasav\sasaf et ha-xatul (cat)
Israel ɡafad\ɡafaz\ɡafal et ha-livjatan (whale)
Matan safaz\safaʦ\safaz et ha-tuki (parrot)
Netanel ɡamas\ɡamat\ɡamaʦ et ha-meduza (jellyfish)
Itamar savaɡ\savak\savak et ha-ʃoʁ (bull)
Erez ɡaɡaf\ɡaɡam\ɡaɡav et ha-dinozauʁ (dinosaur)
Yoav sanav\sanam\sanav et ha-nemala (ant)
Barak ɡaʦav\ɡaʦaf\ɡaʦam et ha-axbeʁoʃ (rat)
Ran sataf\satav\sataf et ha-ʃafan (rabbit)
Tal ɡavad\ɡavaʦ\ɡavaz et ha-nemala (ant)
Yuval samaɡ\samaɡ\samaɡ et ha-naxaʃ (snake)
Alon ɡazav\ɡazav\ɡazaf et ha-akaviʃ (spider)
Or saʁam\saʁam\saʁam et ha-avaz (goose)
Gil ɡaʁan\ɡaʁaʦ\ɡaʁan et ha-dov (bear)
Oded salav\salav\salav et ha-ʃimpanza (chimp)
Roee ɡaxaf\ɡaxaf\ɡaxav et ha-xipuʃit (ladybug)
Oren ʃaɡaf\ʃaɡav\ʃaɡam et ha-ɡdi (young goat)
Oren ɡalak\ɡalaʁ\ɡalaʁ et ha-janʃuf (owl)
Omri ʃaʃaf\ʃaʃam\ʃaʃav et ha-letaa (lizard)
Amir ɡadav\ɡadav\ɡadav et ha-akʁav (scorpion)
Danny ʃalaʁ\ʃalaɡ\ʃalaʁ et ha-ez (nanny goat)
Shachar kaɡam\kaɡaf\kaɡav et ha-boeʃ (polecat)
Amit ʃafaɡ\ʃafak\ʃafak et ha-hipopotam (hippo)
Ariel kakaf\kakav\kakam et ha-kelev (dog)
Omri ʃaʁaf\ʃaʁaf\ʃaʁam et ha-saknai (pelican)
56
Ori kamad\kamad\kamaz et ha-daɡ (fish)
Nir ʃadam\ʃadav\ʃadam et ha-sus (horse)
Gal kadav\kadaf\kadaf et ha-pinɡwin (penguin)
Eyal ʃasam\ʃasav\ʃasav et ha-aʁnav (hare)
Matan kataɡ\katax\katax et ha-nameʁ (leopard)
Yakir ʃaʦam\ʃaʦav\ʃaʦam et ha-jatuʃ (mosquito)
Saar kalaɡ\kalaʁ\kalaɡ et ha-pil (elephant)
Itay ʃaxam\ʃaxam\ʃaxam et ha-kaʁnaf (rhino)
Moti kavaz\kavaz\kavat et ha-hipopotam (hippo)
Dan ʃavaɡ\ʃavaɡ\ʃavaɡ et ha-kaʁnaf (rhino)
Nimrod kafas\kafas\kafas et ha-paʁpaʁ (butterfly)
Noam ʃatav\ʃatav\ʃatav et ha-ɡamal (camel)
Moti kaʁaf\kaʁaf\kaʁaf et ha-xamoʁ (donkey)
Dor ʃazav\ʃazav\ʃazav et ha-xaziʁ (pig)
Yaniv kasav\kasav\kasav et ha-zebʁa (zebra)
Tal taɡaf\taɡam\taɡav et ha-ez (nanny goat)
Daniel kaʦam\kaʦam\kaʦam et ha-boeʃ (polecat)
Ido tavak\tavaʁ\tavaɡ et ha-naxaʃ (snake)
Danny kaʃam\kaʃam\kaʃam et ha-zvuv (fly)
Israel tatav\tatam\tataf et ha-zebʁa (zebra)
Assaf lavax\lavak\lavaɡ et ha-baʁvaz (duck)
Ofir taʦav\taʦam\taʦaf et ha-ʤiʁafa (giraffe)
Idan lafaʁ\lafaɡ\lafak et ha-xamoʁ (donkey)
Yair tafak\tafak\tafaɡ et ha-dolfin (dolphin)
Aviad lalam\lalaf\lalav et ha-xatul (cat)
Ohad tamak\tamaɡ\tamaɡ et ha-kelev (dog)
Erez mafax\mafaɡ\mafaʁ et ha-ʦipoʁ (bird)
Tom tadav\tadav\tadaf et ha-ʦaʁʦaʁ (cricket)
Ofer mavaʁ\mavaɡ\mavak et ha-axbeʁoʃ (rat)
Yoni tazav\tazaf\tazaf et ha-kivsa (sheep)
Or maʁas\maʁaʦ\maʁaz et ha-akaviʃ (spider)
Itay taʁav\taʁav\taʁav et ha-aʁnav (hare)
Alon mamax\mamaɡ\mamaʁ et ha-tavas (peacock)
Boaz talav\talav\talav et ha-tavas (peacock)
Amir mazax\mazak\mazak et ha-snai (squirrel)
Ron ʦaʁaʃ\ʦaʁas\ʦaʁaz et ha-paʁoʃ (flea)
Daniel maʃaʁ\maʃaʁ\maʃaɡ et ha-ʦvi (deer)
Eran ʦafax\ʦafak\ʦafaɡ et ha-livjatan (whale)
Tsachi malaʁ\malaʁ\malaʁ et ha-paʁa (cow)
Barak ʦaxam\ʦaxaf\ʦaxav et ha-kipod (hedgehog)
Yakir nadak\nadaɡ\nadax et ha-oʁev (crow)
Assaf ʦasaf\ʦasam\ʦasav et ha-avaz (goose)
Ben nafak\nafaʁ\nafaɡ et ha-ʃual (fox)
Adam ʦaʦam\ʦaʦaf\ʦaʦav et ha-sus (horse)
Noam nalav\nalam\nalaf et ha-jona (pigeon)
Yossi ʦazav\ʦazam\ʦazaf et ha-baʁbuʁ (swan)
Oded nanam\nanaf\nanav et ha-baʁbuʁ (swan)
Nitsan ʦalaɡ\ʦalaɡ\ʦalaʁ et ha-kaʁiʃ (shark)
Ohad nakas\nakas\nakaʦ et ha-zeev (wolf)
Yotam ʦavak\ʦavak\ʦavaɡ et ha-ʦaʁʦaʁ (cricket)
Tom navaɡ\navaɡ\navak et ha-paʁa (cow)
Evyatar ʦadav\ʦadam\ʦadam et ha-oɡeʁ (hamster)
Ariel nazav\nazam\nazav et ha-tanin (crocodile)
Nimrod ʦamaɡ\ʦamaɡ\ʦamaɡ et ha-ʃimpanza (chimp)
Ido naʃaɡ\naʃaɡ\naʃaɡ et ha-kenɡeʁu (kangaroo)
Oz ʦanav\ʦanav\ʦanav et ha-dinozauʁ (dinosaur)
Shay naɡam\naɡam\naɡam et ha-ʦfaʁdea (frog)
Ram xalaɡ\xalaʁ\xalaʁ et ha-jatuʃ (mosquito)
Guy natam\natam\natam et ha-taiʃ (male goat)
Ofir xatak\xatak\xataɡ et ha-xasida (stork)
Yaron namaʁ\namaʁ\namaʁ et ha-axbaʁ (mouse)
Aviv xazav\xazaf\xazaf et ha-pil (elephant)
Ofer pakaz\pakat\pakaʃ et ha-ʃablul (snail)
Roee xaxav\xaxaf\xaxav et ha-kenɡeʁu (kangaroo)
Yaniv paɡan\paɡas\paɡaʦ et ha-ɡamal (camel)
Gil xaʁan\xaʁal\xaʁan et ha-eɡel (calf)
Evyatar zafak\zafax\zafaʁ et ha-ʃafan (rabbit)
Avi xafal\xafal\xafad et ha-daɡ (fish)
Tsachi zazav\zazaf\zazam et ha-xaziʁ (pig)
Omer xakav\xakav\xakaf et ha-xasida (stork)
Lior zakav\zakam\zakam et ha-oɡeʁ (hamster)
Shachar xavan\xavaz\xavan et ha-meduza (jellyfish)
Yoni zavaʁ\zavaʁ\zavak et ha-aʁje (lion)
Lior xanaʁ\xanaʁ\xanaʁ et ha-ʦvi (deer)
Yonatan zalav\zalav\zalam et ha-atalef (bat)
Ben xamaz\xamaz\xamaz et ha-taʁneɡol (rooster)
Aviad zaʁaf\zaʁaf\zaʁaf et ha-tiɡʁis (tiger)
57
Appendix H: Word-likelihood judgment experiment: The triplets with significant
difference in ratings
bʁ
bʦ
dd
df
dʁ
dx
pd
pk
pl
pm
pʃ
Average
SD
baʁal
3.43
1.79
baʁan
4.25
2.02
baʁas
5.39
baʦaɡ
P-value
Average
SD
paʦaɡ
3.80
1.32
paʦaɡ
3.07
1.59
1.75
paʦak
4.56
1.03
3.45
1.69
ʁaʁaf
3.29
1.82
baʦaɡ
2.36
1.22
ʁaʁam
2.07
1.44
baʦax
4.07
1.73
ʁaʁav
1.31
0.70
dadaf
3.08
2.22
ʃafaɡ
3.43
1.83
dadam
3.41
2.00
ʃafak
4.41
1.46
dadav
1.82
1.29
ʃafak
4.94
1.92
dafaʁ
4.69
1.35
saɡaʃ
3.25
2.14
dafaʁ
4.83
1.79
saɡat
3.71
1.69
dafax
3.43
1.79
saɡaʦ
2.25
1.57
daʁaf
5.18
1.88
ʃalaɡ
5.06
1.61
daʁaf
5.00
1.53
ʃalaʁ
3.78
1.59
daʁav
3.86
1.75
ʃalaʁ
4.06
2.10
daxam
3.14
1.79
savaɡ
5.14
1.41
daxav
5.06
1.85
savak
3.58
1.73
daxav
4.94
1.30
savak
3.67
1.50
padaɡ
3.12
1.36
tataf
3.18
1.78
padak
5.12
1.80
tatam
3.73
1.75
padaʁ
3.93
1.58
tatav
2.25
1.81
pakaʃ
4.50
1.70
ʦaʦaf
2.89
1.75
pakat
4.67
1.67
ʦaʦam
2.21
1.63
pakaz
3.00
1.81
ʦaʦav
1.70
1.49
palak
5.23
1.36
palaʁ
4.15
1.68
palaʁ
4.07
1.58
pamaɡ
1.92
0.64
pamak
3.50
1.15
pamax
4.24
1.92
paʃaɡ
3.27
1.94
paʃak
4.43
0.94
paʃak
4.83
1.72
pʦ
0.015
ʁʁ
0.032
ʃf
0.037
sɡ
0.015
ʃl
0.024
0.002
sv
0.004
tt
ʦʦ
0.022
0.038
0.000
0.011
58
P-value
0.012
0.001
0.029
0.047
0.043
0.005
0.017
0.020
Appendix I: Word-likelihood judgment experiment: Results
Average Rating
Average Rating
Average Rating
Average Rating
ss
2.05
ɡf
3.49
ɡd
4.05
nt
4.63
ʦs
2.21
sx
3.50
bn
4.06
ʃv
4.64
kɡ
2.27
pɡ
3.52
dl
4.08
ɡʁ
4.64
ʃʃ
2.29
sm
3.55
bd
4.09
nɡ
4.64
zz
2.51
mv
3.55
ʦx
4.09
ʦf
4.64
td
2.53
bs
3.56
ps
4.10
xv
4.64
tʦ
2.60
zk
3.60
ɡv
4.13
ʃʁ
4.65
bv
2.65
xt
3.68
xk
4.16
mʃ
4.73
ʦʁ
2.65
ʁl
3.70
ʦn
4.20
kd
4.74
kk
2.66
nʃ
3.70
st
4.20
xm
4.74
sz
2.66
xl
3.74
ʦv
4.21
ɡx
4.79
ɡɡ
2.67
pn
3.76
ɡz
4.22
zʁ
4.79
ll
2.68
ɡl
3.77
tɡ
4.23
nz
4.83
mm
2.69
bʃ
3.79
nv
4.24
bx
4.84
bt
2.69
zf
3.79
kt
4.25
bl
4.87
nn
2.71
dm
3.80
xz
4.27
kf
5.00
ʦz
2.73
ks
3.80
kʦ
4.28
kʁ
5.10
tz
2.73
zv
3.83
ʃɡ
4.28
xf
5.22
sf
2.80
ʁf
3.84
ʃd
4.29
px
5.39
pf
2.81
lf
3.85
zl
4.30
ʃx
5.41
tm
2.91
sd
3.85
mʁ
4.30
bf
2.93
ml
3.86
kʃ
4.31
ʃʦ
2.96
kl
3.87
tf
4.31
xx
2.96
nd
3.88
sʁ
4.33
dz
3.00
ʃt
3.90
bz
4.33
bm
3.02
pt
3.91
ɡm
4.34
ʃs
3.15
ʁv
3.93
nm
4.38
pz
3.16
kv
3.93
nk
4.39
pv
3.21
km
3.94
nf
4.39
ɡʦ
3.27
dv
3.96
mz
4.42
ʁn
3.31
tl
3.96
ʃz
4.46
bk
3.42
xn
3.98
tv
4.48
nl
3.42
lv
3.98
xʁ
4.48
mf
3.43
sn
4.02
sl
4.49
ʦd
3.44
ʦl
4.02
pʁ
4.52
ʦm
3.45
bɡ
4.04
tʁ
4.60
59
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תקציר
בשפות שמיות ,עיצורים הומורגניים (עיצורים החולקים מקום חיתוך) נוטים שלא להופיע באותו
גזע ( .)Greenberg 1950מחקרים קודמים (כדוגמת )Frisch et al. 2004 ;McCarthy 1981, 1986
מציעים כי הגבלות אלו נובעות מדמיון בין עיצורים ,כך שככל ששני עיצורים (הומורגניים) דומים
יותר זה לזה ,כך הסבירות שיופיעו באותו גזע נמוכה יותר .המחקר הנוכחי בוחן את ההגבלות על
מופעי עיצורים במערכת הפועל בעברית .אני שואלת כיצד דמיון בין עיצורים תורם להגבלות ,והאם
ההגבלות נובעות מאילוצים אוניברסליים או שהן מושפעות מגורמים תלויי שפה.
למחקר שלושה חלקים עיקריים .ראשית ,התאמתי את מודל הדמיון של Frisch et al.
) (2004למצאי העיצורים בעברית .שנית ,ניתחתי את לקסיקון מערכת הפועל בעברית ,תוך
התמקדות בעיצורים הראשון והשני של הגזע ( )C1-C2בבניינים קל ופיעל .הניתוח הראה מתאם
ברמת מובהקות גבוהה בין מודל הדמיון והלקסיקון ,וכן מציע כי למקום החיתוך של העיצורים יש
השפעה רבה ,בהשוו אה לתכוניות אחרות ,בקביעת ההגבלות .על מנת לחזק ולהרחיב את הניתוח
הלקסיקלי ,ערכתי שני ניסויים פסיכובלשניים :מטלת זיהוי לקסיקלי ומטלת שיפוטי סבירות עבור
לא -מילים ,שניהם בוחנים את ההגבלות במערכת הפונולוגית של הדוברים .ניתוח שיפוטי הסבירות
הראה מתאם ברמת מובהקות גבוהה בין שיפוטי הדוברים ומודל הדמיון מחד ,ובין שיפוטי
הדוברים ומחקר הלקסיקון מאידך .כמו כן ,הניסויים גם כן מדגישים את תפקיד מקום החיתוך של
העיצורים בהגבלות על מופעים.
ממצאים אלו מציעים כי ישנן הגבלות מבוססות דמיון על שני העיצורים הראשונים בגזע
הפעלים ב עברית ,גם בלקסיקון וגם במערכת הפונולוגית של הדוברים .כמו כן ,הם מציעים כי
למקום החיתוך של העיצורים השפעה רבה בקביעת ההגבלות ,כך ששני עיצורים החולקים מקום
חיתוך ראשי נוטים שלא להופיע באותו גזע .אף על פי כן ,הניסויים אינם יכולים להציע האם השפעת
הדמיון על המע רכת הפונולוגית היא ישירה ,או עקיפה דרך השפעות לקסיקליות.
הפקולטה למדעי הרוח ע"ש לסטר וסאלי אנטין
החוג לבלשנות
תפקיד הדמיון בהגבלות על רצפי עיצורים:
עדות ממערכת הפועל בעברית
חיבור זה הוגש כעבודת גמר לקראת התואר
"מוסמך אוניברסיטה" באוניברסיטת ת"א
על ידי
הדס יברכיהו
העבודה הוכנה בהדרכת:
פרופ' אותי בת-אל
ד"ר אוון-גרי כהן
דצמבר 4102