Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the ACL, pages 689–696,
Sydney, July 2006. c©2006 Association for Computational Linguistics
Noun Phrase Chunking in Hebrew 
Influence of Lexical and Morphological Features 
 
 
Yoav Goldberg  and  Meni Adler  and  Michael Elhadad 
Computer Science Department 
Ben Gurion University of the Negev 
P.O.B 653 Be'er Sheva 84105, Israel  
{yoavg,adlerm,elhadad}@cs.bgu.ac.il 
 
  
 
Abstract 
We present a method for Noun Phrase 
chunking in Hebrew. We show that the 
traditional definition of base-NPs as non-
recursive noun phrases does not apply in 
Hebrew, and propose an alternative defi-
nition of Simple NPs.  We review syntac-
tic properties of Hebrew related to noun 
phrases, which indicate that the task of 
Hebrew SimpleNP chunking is harder 
than base-NP chunking in English. As a 
confirmation, we apply methods known 
to work well for English to Hebrew data. 
These methods give low results (F from 
76 to 86) in Hebrew. We then discuss our 
method, which applies SVM induction 
over lexical and morphological features. 
Morphological features improve the av-
erage precision by ~0.5%, recall by ~1%, 
and F-measure by ~0.75, resulting in a 
system with average performance of 93% 
precision, 93.4% recall and 93.2 F-
measure.* 
1 Introduction 
Modern Hebrew is an agglutinative Semitic lan-
guage, with rich morphology.  Like most other 
non-European languages, it lacks NLP resources 
and tools, and specifically there are currently no 
available syntactic parsers for Hebrew.  We ad-
dress the task of NP chunking in Hebrew as a 
                                                 
* This work was funded by the Israel Ministry of Sci-
ence and Technology under the auspices of the 
Knowledge Center for Processing Hebrew.  Addi-
tional funding was provided by the Lynn and William 
Frankel Center for Computer Sciences.  
first step to fulfill the need for such tools.  We 
also illustrate how this task can successfully be 
approached with little resource requirements, and 
indicate how the method is applicable to other 
resource-scarce languages. 
NP chunking is the task of labelling noun 
phrases in natural language text. The input to this 
task is free text with part-of-speech tags.  The 
output is the same text with brackets around base 
noun phrases.  A base noun phrase is an NP 
which does not contain another NP (it is not re-
cursive).  NP chunking is the basis for many 
other NLP tasks such as shallow parsing, argu-
ment structure identification, and information 
extraction 
We first realize that the definition of base-NPs 
must be adapted to the case of Hebrew (and 
probably other Semitic languages as well) to cor-
rectly handle its syntactic nature.  We propose 
such a definition, which we call simple NPs and 
assess the difficulty of chunking such NPs by 
applying methods that perform well in English to 
Hebrew data.  While the syntactic problem in 
Hebrew is indeed more difficult than in English, 
morphological clues do provide additional hints, 
which we exploit using an SVM learning 
method.  The resulting method reaches perform-
ance in Hebrew comparable to the best results 
published in English. 
2 Previous Work 
Text chunking (and NP chunking in particular), 
first proposed by Abney (1991), is a well studied 
problem for English. The CoNLL2000 shared 
task (Tjong Kim Sang et al., 2000) was general 
chunking. The best result achieved for the shared 
task data was by Zhang et al (2002), who 
achieved NP chunking results of 94.39% preci-
sion, 94.37% recall and 94.38 F-measure using a 
689
generalized Winnow algorithm, and enhancing 
the feature set with the output of a dependency 
parser. Kudo and Matsumoto (2000) used an 
SVM based algorithm, and achieved NP chunk-
ing results of 93.72% precision, 94.02% recall 
and 93.87 F-measure for the same shared task 
data, using only the words and their PoS tags. 
Similar results were obtained using Conditional 
Random Fields on similar features (Sha and 
Pereira, 2003). 
The NP chunks in the shared task data are 
base-NP chunks – which are non-recursive NPs, 
a definition first proposed by Ramshaw and 
Marcus (1995). This definition yields good NP 
chunks for English, but results in very short and 
uninformative chunks for Hebrew (and probably 
other Semitic languages). 
Recently, Diab et al (2004) used SVM based 
approach for Arabic text chunking.  Their chunks 
data was derived from the LDC Arabic TreeBank 
using the same program that extracted the chunks 
for the shared task.  They used the same features 
as Kudo and Matsumoto (2000), and achieved 
over-all chunking performance of 92.06% preci-
sion, 92.09% recall and 92.08 F-measure (The 
results for NP chunks alone were not reported).  
Since Arabic syntax is quite similar to Hebrew, 
we expect that the issues reported below apply to 
Arabic results as well. 
3 Hebrew Simple NP Chunks 
The standard definition of English base-NPs is 
any noun phrase that does not contain another 
noun phrase, with possessives treated as a special 
case, viewing the possessive marker as the first 
word of a new base-NP (Ramshaw and Marcus, 
1995).  To evaluate the applicability of this defi-
nition to Hebrew, we tested this definition on the 
Hebrew TreeBank (Sima’an et al, 2001) pub-
lished by the Hebrew Knowledge Center. We 
extracted all base-NPs from this TreeBank, 
which is similar in genre and contents to the 
English one.  This results in extremely simple 
chunks.  
 
English 
BaseNPs 
Hebrew 
BaseNPs 
Hebrew 
SimpleNPs 
Avg # of words 2.17 1.39 2.49 
% length 1 30.95 63.32 32.83 
% length 2 39.35 35.48 32.12 
% length 3 18.68 0.83 14.78 
% length 4 6.65 0.16 9.47 
% length 5 2.70 0.16 4.56 
% length > 5 1.67 0.05 6.22 
Table 1.  Size of Hebrew and English NPs 
Table 1 shows the average number of words in a 
base-NP for English and Hebrew.  The Hebrew 
chunks are basically one-word groups around 
Nouns, which is not useful for any practical pur-
pose, and so we propose a new definition for He-
brew NP chunks, which allows for some nested-
ness. We call our chunks Simple NP chunks.  
3.1 Syntax of NPs in Hebrew 
One of the reasons the traditional base-NP defi-
nition fails for the Hebrew TreeBank is related to 
syntactic features of Hebrew – specifically, 
smixut (construct state – used to express noun 
compounds), definite marker and the expression 
of possessives. These differences are reflected to 
some extent by the tagging guidelines used to 
annotate the Hebrew Treebank and they result in 
trees which are in general less flat than the Penn 
TreeBank ones.  
Consider the example base noun phrase [The 
homeless people]. The Hebrew equivalent is 
(1)  	

  
 which by the non-recursive NP definition will be 
bracketed as: 

	
 , or, loosely translating 
back to English: [the home]less [people].  
In this case, the fact that the bound-morpheme 
less appears as a separate construct state word 
with its own definite marker (ha-) in Hebrew 
would lead the chunker to create two separate 
NPs for a simple expression.  We present below 
syntactic properties of Hebrew which are rele-
vant to NP chunking. We then present our defini-
tion of Simple NP Chunks.  
 
Construct State: The Hebrew genitive case is 
achieved by placing two nouns next to each other. 
This is called “noun construct”, or smixut. The 
semantic interpretation of this construct is varied 
(Netzer and Elhadad, 1998), but it specifically 
covers possession. The second noun can be 
treated as an adjective modifying the next noun. 
The first noun is morphologically marked in a 
form known as the construct form (denoted by 
const). The definite article marker is placed on 
the second word of the construction: 
(2)  
 beit sefer / house-[const] book 
 School 
(3)  
 beit ha-sefer / house-[const] the-book 
 The school 
 
The construct form can also be embedded: 
(4) 	



 
690
misrad ro$ ha-mem$ala  
Office-[const poss] head-[const] the-government 
The prime-minister’s office 
 
Possessive: the smixut form can be used to indi-
cate possession. Other ways to express posses-
sion include the possessive marker  - ‘$el’ / 
‘of’ - (5), or adding a possessive suffix on the 
noun (6). The various forms can be mixed to-
gether, as in (7): 
(5) 	
 
ha-bait $el-i / the-house of-[poss 1st person] 
My house 
(6)  
beit-i / house-[poss 1st person] 
My house 
(7) 	

	


  
misrad-o $el ro$ ha-mem$ala 
Office-[poss 3rd] of head-[const] the-government 
The prime minister office 
 
Adjective: Hebrew adjectives come after the 
noun, and agree with it in number, gender and 
definite marker: 
(8)  
ha-tapu’ah ha-yarok / the-Apple the-green 
The green apple 
 
Some aspects of the predicate structure in He-
brew directly affect the task of NP chunking, as 
they make the decision to “split” NPs more or 
less difficult than in English. 
 
Word order and the preposition 'et': Hebrew 
sentences can be either in SVO or VSO form. In 
order to keep the object separate from the sub-
ject, definite direct objects are marked with the 
special preposition 'et', which has no analog in 
English.  
 
Possible null equative: The equative form in 
Hebrew can be null. Sentence (9) is a non-null 
equative, (10) a null equative, while (11) and 
(12) are predicative NPs, which look very similar 
to the null-equative form: 
 
(9) 	
 
ha-bait hu gadol 
The-house is big 
The house is big 
 
(10) 	
 
ha-bait gadol 
The-house big 
The house is big 
 
(11) 	
 
bait gadol 
House big 
A big house 
(12) 	
 
ha-bait ha-gadol 
The-house the-big 
The big house 
 
Morphological Issues: In Hebrew morphology, 
several lexical units can be concatenated into a 
single textual unit.  Most prepositions, the defi-
nite article marker and some conjunctions are 
concatenated as prefixes, and possessive pro-
nouns and some adverbs are concatenated as suf-
fixes.  The Hebrew Treebank is annotated over a 
segmented version of the text, in which prefixes 
and suffixes appear as separate lexical units.  On 
the other hand, many bound morphemes in Eng-
lish appear as separate lexical units in Hebrew.  
For example, the English morphemes re-, ex-, 
un-, -less, -like, -able, appear in Hebrew as sepa-
rate lexical units – , 	, 


 , , , 
, . 

  
In our experiment, we use as input to the 
chunker the text after it has been morphologi-
cally disambiguated and segmented. Our 
analyzer provides segmentation and PoS tags 
with 92.5% accuracy and full morphology with 
88.5% accuracy (Adler and Elhadad, 2006). 
3.2 Defining Simple NPs 
Our definition of Simple NPs is pragmatic. We 
want to tag phrases that are complete in their 
syntactic structure, avoid the requirement of tag-
ging recursive structures that include full clauses 
(relative clauses for example) and in general, tag 
phrases that have a simple denotation. To estab-
lish our definition, we start with the most com-
plex NPs, and break them into smaller parts by 
stating what should not appear inside a Simple 
NP. This can be summarized by the following 
table: 
 
Outside SimpleNP Exceptions 
Prepositional Phrases 
Relative Clauses 
Verb Phrases 
Apposition1 
Some conjunctions 
(Conjunctions are 
marked according to the 
TreeBank guidelines)2. 
% related PPs are 
allowed:  

5% of the sales 
 
Possessive  - '$el' / 
'of' - is not consid-
ered a PP 
Table 2.   Definition of Simple NP chunks 
Examples for some Simple NP chunks resulting 
from that definition: 
 
                                                 
1 Apposition structure is not annotated in the TreeBank. As 
a heuristic, we consider every comma inside a non conjunct-
ive NP which is not followed by an adjective or an adjective 
phrase to be marking the beginning of an apposition. 
2 As a special case, Adjectival Phrases and possessive con-
junctions are considered to be inside the Simple NP.  
691
	


	




[This phenomenon] was highlighted yesterday at 
[the labor and welfare committee-const of the 
Knesset] that dealt with [the topic-const of for-
eign workers employment-const]. 
 
	

		

	

			
	
3
	

[The employers] do not expect to succeed in at-
tracting [a significant number of Israeli workers] 
for [the fruit-picking] because of [the low salaries] 
paid for [this work]. 
 
This definition can also yield some rather long 
and complex chunks, such as: 
 

	
 		

[The conquests of Genghis Khan and his Mongol 
Tartar army] 

		


!		



	



	

!
	
	
According to [reports of local government offi-
cials], [factories] on [Tartar territory] earned in 
[the year] that passed [a sum of 3.7 billion Rb (2.2 
billion dollars)], which [Moscow] took [almost all]. 
 
Note that Simple NPs are split, for example, by 
the preposition ‘on’ ([factories] on [Tartar terri-
tory]), and by a relative clause ([a sum of 3.7Bn 
Rb] which [Moscow] took [almost all]). 
3.3 Hebrew Simple NPs are harder 
than English base NPs 
The Simple NPs derived from our definition are 
highly coherent units, but are also more complex 
than the non-recursive English base NPs.  
As can be seen in Table 1, our definition of Sim-
ple NP yields chunks which are on average con-
siderably longer than the English chunks, with 
about 20% of the chunks with 4 or more words 
(as opposed to about 10% in English) and a sig-
nificant portion (6.22%) of chunks with 6 or 
more words (1.67% in english). 
Moreover, the baseline used at the CoNLL 
shared task4 (selecting the chunk tag which was 
most frequently associated with the current PoS) 
                                                 
3 For readers familiar with Hebrew and feel that  is 
an adjective and should be inside the NP, we note that this is 
not the case –  here is actually a Verb in the Beinoni 
form and the definite marker is actually used as relative 
marker. 
4 http://www.cnts.ua.ac.be/conll2000/chunking/ 
gives far inferior results for Hebrew SimpleNPs 
(see Table 3).  
4 Chunking Methods 
4.1 Baseline Approaches  
We have experimented with different known 
methods for English NP chunking, which re-
sulted in poor results for Hebrew. We describe 
here our experiment settings, and provide the 
best scores obtained for each method, in com-
parison to the reported scores for English.  
All tests were done on the corpus derived from 
the Hebrew Tree Bank. The corpus contains 
5,000 sentences, for a total of 120K tokens (ag-
glutinated words) and 27K NP chunks (more de-
tails on the corpus appear below).  The last 500 
sentences were used as the test set, and all the 
other sentences were used for training. The re-
sults were evaluated using the CoNLL shared 
task evaluation tools 5 . The approaches tested 
were Error Driven Pruning (EDP) (Cardie and 
Pierce, 1998) and Transformational Based Learn-
ing of IOB tagging (TBL) (Ramshaw and Mar-
cus, 1995). 
The Error Driven Pruning method does not 
take into account lexical information and uses 
only the PoS tags.  For the Transformation Based 
method, we have used both the PoS tag and the 
word itself, with the same templates as described 
in (Ramshaw and Marcus, 1995). We tried the 
Transformational Based method with more fea-
tures than just the PoS and the word, but ob-
tained lower performance. Our best results for 
these methods, as well as the CoNLL baseline 
(BASE), are presented in Table 3.  These results 
confirm that the task of Simple NP chunking is 
harder in Hebrew than in English.   
4.2 Support Vector Machines 
We chose to adopt a tagging perspective for 
the Simple NP chunking task, in which each 
word is to be tagged as either B, I or O depend-
ing on wether it is in the Beginning, Inside, or 
Outside of the given chunk, an approach first 
taken by Ramshaw and Marcus (1995), and 
which has become the de-facto standard for this 
task. Using this tagging method, chunking be-
comes a classification problem – each token is 
predicted as being either I, O or B, given features 
from a predefined linguistic context (such as the 
                                                 
5http://www.cnts.ua.ac.be/conll2000/chunking/conllev
al.txt 
692
words surrounding the given word, and their PoS 
tags). 
One model that allows for this prediction is 
Support Vector Machines - SVM (Vapnik, 
1995). SVM is a supervised machine learning 
algorithm which can handle gracefully a large set 
of overlapping features. SVMs learn binary clas-
sifiers, but the method can be extended to multi-
class classification (Allwein et al., 2000; Kudo 
and Matsumoto, 2000).   
SVMs have been successfully applied to many 
NLP tasks since (Joachims, 1998), and specifi-
cally for base phrase chunking (Kudo and Ma-
tsumoto, 2000; 2003).  It was also successfully 
used in Arabic (Diab et al., 2004).  
The traditional setting of SVM for chunking 
uses for the context of the token to be classified a 
window of two tokens around the word, and the 
features are the PoS tags and lexical items (word 
forms) of all the tokens in the context. Some set-
tings (Kudo and Matsumoto, 2000) also include 
the IOB tags of the two “previously tagged” to-
kens as features (see Fig. 1). 
This setting (including the last 2 IOB tags) 
performs nicely for the case of Hebrew Simple 
NPs chunking as well. 
Linguistic features are mapped to SVM fea-
ture vectors by translating each feature such as 
“PoS at location n-2 is NOUN” or “word at loca-
tion n+1 is DOG” to a unique vector entry, and 
setting this entry to 1 if the feature occurs, and 0 
otherwise. This results in extremely large yet 
extremely sparse feature vectors. 
 
English 
BaseNPs 
Hebrew Sim-
pleNPs 
Method 
Prec Rec Prec Rec F 
BASE 72.58 82.14 64.7 75.4 69.78 
EDP 92.7 93.7 74.6 78.1 76.3 
TBL 91.3 91.8 84.7 87.7 86.2 
Table 3.  Baseline results for Simple NP chunking 
SVM Chunking in Hebrew 
 
WORD POS CHUNK 
 NA B-NP 
 NOUN I-NP 
 PREP O 
	
 NAME B-NP 
 PREP O 
 NA B-NP 
 NOUN I-NP 
Figure 1.  Linguistic features considered in the 
basic SVM setting for Hebrew 
4.3 Augmentation of Morphological 
Features  
Hebrew is a morphologically rich language. Re-
cent PoS taggers and morphological analyzers 
for Hebrew (Adler and Elhadad, 2006) address 
this issue and provide for each word not only the 
PoS, but also full morphological features, such as 
Gender, Number, Person, Construct, Tense, and 
the affixes' properties. Our system, currently, 
computes these features with an accuracy of 
88.5%. 
Our original intuition is that the difficulty of 
Simple NP chunking can be overcome by relying 
on morphological features in a small context.  
These features would help the classifier decide 
on agreement, and split NPs more accurately.  
Since SVMs can handle large feature sets, we 
utilize additional morphological features. In par-
ticular, we found the combination of the Number 
and the Construct features to be most effective in 
improving chunking results.  Indeed, our experi-
ments show that introducing morphological fea-
tures improves chunking quality by as much as 
3-point in F-measure when compared with lexi-
cal and PoS features only. 
5 Experiment 
5.1 The Corpus 
The Hebrew TreeBank6 consists of 4,995 hand 
annotated sentences from the Ha’aretz newspa-
per.  Besides the syntactic structure, every word 
is PoS annotated, and also includes morphologi-
cal features.  The words in the TreeBank are 
segmented: 

  (instead of 
 ). 
Our morphological analyzer also provides such 
segmentation.  
We derived the Simple NPs structure from the 
TreeBank using the definition given in Section 
3.2. We then converted the original Hebrew 
TreeBank tagset to the tagset of our PoS tagger.  
For each token, we specify its word form, its 
PoS, its morphological features, and its correct 
IOB tag. The result is the Hebrew Simple NP 
chunks corpus 7 . The corpus consists of 4,995 
sentences, 27,226 chunks and 120,396 seg-
mented tokens. 67,919 of these tokens are cov-
ered by NP chunks. A sample annotated sentence 
is given in Fig. 2. 
 
 
                                                 
6http://mila.cs.technion.ac.il/website/english/resources
/corpora/treebank/index.html 
7 http://www.cs.bgu.ac.il/~nlpproj/chunking 
Feature 
 Set 
 
Estimated Tag 
693
 PREPOSITION NA NA N NA N NA N NA NA O 
 DEF_ART NA NA N NA N NA N NA NA B-NP 
 NOUN M S N NA N NA N NA NA I-NP 
 AUXVERB M S N 3 N PAST N NA NA O 
 ADJECTIVE M S N NA N NA N NA NA O 
	 ADVERB NA NA N NA N NA N NA NA O 

	 VERB NA NA N NA Y TOINF N NA NA O 

 ET_PREP NA NA N NA N NA N NA NA B-NP 
 DEF_ART NA NA N NA N NA N NA NA I-NP 
 NOUN F S N NA N NA N NA NA I-NP 
. PUNCUATION NA NA N NA N NA N NA NA O 
Figure 2.  A Sample annotated sentence 
5.2 Morphological Features: 
The PoS tagset we use consists of 22 tags:  
 
ADJECTIVE ADVERB ET_PREP 
AUXVERB CONJUNCTION DEF_ART 
DETERMINER EXISTENTIAL INTERJECTION 
INTEROGATIVE MODAL NEGATION 
PARTICLE NOUN NUMBER 
PRONOUN PREFIX PREPOSITION 
UNKNOWN PROPERNAME PUNCTUATION 
VERB   
 
For each token, we also supply the following 
morphological features (in that order): 
 
Feature Possible Values 
Gender (M)ale, (F)emale,  
(B)oth (unmarked case), (NA) 
Number (S)ingle, (P)lurar, (D)ual,  
can be (ALL), (NA) 
Construct (Y)es, (N)o 
Person (1)st, (2)nd, (3)rd, (123)all, (NA) 
To-Infinitive (Y)es, (N)o 
Tense Past, Present, Future, Beinoni, 
Imperative, ToInf, BareInf 
(has) Suffix (Y)es, (N)o 
Suffix-Num (M)ale, (F)emale, (B)oth, (NA) 
Suffix-Gen (S)ingle, (P)lurar, (D)ual, (DP)-
dual plural, can be (ALL), (NA) 
 
As noted in (Rambow and Habash 2005), one 
cannot use the same tagset for a Semitic lan-
guage as for English.  The tagset we have de-
rived has been extensively validated through 
manual tagging by several testers and cross-
checked for agreement.  
5.3 Setup and Evaluation 
For all the SVM chunking experiments, we use 
the YamCha 8  toolkit (Kudo and Matsumoto, 
2003).  We use forward moving tagging, using 
standard SVM with polynomial kernel of degree 
2, and C=1.  For the multiclass classification, we 
                                                 
8 http://chasen.org/~taku/software/yamcha/ 
use pairwise voting.  For all the reported experi-
ments, we chose the context to be a –2/+2 tokens 
windows, centered at the current token.  
We use the standard metrics of accuracy (% of 
correctly tagged tokens), precision, recall and F-
measure, with the only exception of normalizing 
all punctuation tokens from the data prior to 
evaluation, as the TreeBank is highly inconsis-
tent regarding the bracketing of punctuations, 
and we don’t consider the exclusions/inclusions 
of punctuations from our chunks to be errors 
(i.e., “[a book ,] [an apple]” “[a book] , [an ap-
ple]” and “[a book] [, an apple]” are all equiva-
lent chunkings in our view). 
All our development work was done with the 
first 500 sentences allocated for testing, and the 
rest for training.  For evaluation, we used a 10-
fold cross-validation scheme, each time with dif-
ferent consecutive 500 sentences serving for test-
ing and the rest for training.  
5.4 Features Used 
We run several SVM experiments, each with the 
settings described in section 5.3, but with a dif-
ferent feature set.  In all of the experiments the 
two previously tagged IOB tags were included in 
the feature set. In the first experiment (denoted 
WP) we considered the word and PoS tags of the 
context tokens to be part of the feature set.  
In the other experiments, we used different 
subsets of the morphological features of the to-
kens to enhance the features set.  We found that 
good results were achieved by using the Number 
and Construct features together with the word 
and PoS tags (we denote this WPNC). Bad re-
sults were achieved when using all the morpho-
logical features together. The usefulness of fea-
ture sets was stable across all tests in the ten-fold 
cross validation scheme. 
5.5 Results 
We discuss the results of the WP and WPNC 
experiments in details, and also provide the re-
sults for the WPG (using the Gender feature), 
and ALL (using all available morphological fea-
tures) experiments, and P (using only PoS tags). 
As can be seen in Table 4, lexical information 
is very important: augmenting the PoS tag with 
lexical information boosted the F-measure from 
77.88 to 92.44.  The addition of the extra mor-
phological features of Construct and Number 
yields another increase in performance, resulting 
in a final F-measure of 93.2%. Note that the ef-
fect of these morphological features on the over-
all accuracy (the number of BIO tagged cor-
694
rectly) is minimal (Table 5), yet the effect on the 
precision and recall is much more significant.  It 
is also interesting to note that the Gender feature 
hurts performance, even though Hebrew has 
agreement on both Number and Gender.  We do 
not have a good explanation for this observation 
– but we are currently verifying the consistency 
of the gender annotation in the corpus (in par-
ticular, the effect of the unmarked gender tag). 
We performed the WP and WPNC experiment 
on two forms of the corpus: (1) WP,WPNC using 
the manually tagged morphological features in-
cluded in the TreeBank and (2) WPE, WPNCE 
using the results of our automatic morphological 
analyzer, which includes about 10% errors (both 
in PoS and morphological features). With the 
manual morphology tags, the final F-measure is 
93.20, while it is 91.40 with noise.  Interestingly, 
the improvement brought by adding morphologi-
cal features to chunking in the noisy case 
(WPNCE) is almost 3.0 F-measure points (as 
opposed to 0.758 for the "clean" morphology 
case WPNC). 
 
Features Acc Prec Rec F 
P 91.77 77.03 78.79 77.88 
WP 97.49 92.54 92.35 92.44 
WPE 94.87 89.14 87.69 88.41 
WPG 97.41 92.41 92.22 92.32 
ALL 96.68 90.21 90.60 90.40 
WPNC 97.61 92.99 93.41 93.20 
WPNCE 96.99 91.49 91.32 91.40 
Table 4. SVM results for Hebrew 
 
Features Prec Rec F 
WPNC 0.456 1.058 0.758 
WPNCE 2.35 3.60 2.99 
Table 5.  Improvement over WP 
5.6 Error Analysis and the Effect of 
Morphological Features 
We performed detailed error analysis on the 
WPNC results for the entire corpus. At the indi-
vidual token level, Nouns and Conjunctions 
caused the most confusion, followed by Adverbs 
and Adjectives. Table 6 presents the confusion 
matrix for all POSs with a substantial amount of 
errors. IO means that the correct chunk tag was 
I, but the system classified it as O.  By examin-
ing the errors on the chunks level, we identified 7 
common classes of errors:  
Conjunction related errors: bracketing “[a] 
and [b]” instead of “[a and b]” and vice versa. 
Split errors: bracketing [a][b] instead of [a b] 
Merge errors: bracketing [a b] instead of [a][b] 
Short errors: bracketing “a [b]” or “[a] b” in-
stead of [a b] 
Long errors: bracketing “[a b]” instead of “[a] 
b” or “a [b]” 
Whole Chunk errors: either missing a whole 
chunk, or bracketing something which doesn’t 
overlap with a chunk at all (extra chunk). 
Missing/ExtraToken errors: this is a general-
ized form of conjunction errors: either “[a] T 
[b]” instead of “[a T b]” or vice versa, where T 
is a single token. The most frequent of such 
words (other than the conjuncts) was   - the 
possessive '$el'.  
 
Table 6.  WPNC Confusion Matrix 
The data in Table 6 suggests that Adverbs and 
Adjectives related errors are mostly of the 
“short” or “long” types, while the Noun (includ-
ing proper names and pronouns) related errors 
are of the “split” or “merge” types. 
The most frequent error type was conjunction 
related, closely followed by split and merge. 
Much less significant errors were cases of extra 
Adverbs or Adjectives at the end of the chunk, 
and missing adverbs before or after the chunk.  
Conjunctions are a major source of errors for 
English chunking as well (Ramshaw and Marcus, 
1995, Cardie and Pierce, 1998)9, and we plan to 
address them in future work. The split and merge 
errors are related to argument structure, which 
can be more complicated in Hebrew than in Eng-
lish, because of possible null equatives. The too-
long and too-short errors were mostly attachment 
related.  Most of the errors are related to linguis-
tic phenomena that cannot be inferred by the lo-
calized context used in our SVM encoding. We 
examine the types of errors that the addition of 
                                                 
9 Although base-NPs are by definition non-recursive, 
they may still contain CCs when the coordinators are 
‘trapped’: “[securities and exchange commission]” or 
conjunctions of adjectives. 
695
Number and Construct features fixed. Table 7 
summarizes this information. 
 
ERROR WP WPNC # Fixed % Fixed 
CONJUNCTION 256 251 5 1.95 
SPLIT 198 225 -27 -13.64 
MERGE 366 222 144 39.34 
LONG (ADJ AFTER) 120 117 3 2.50 
EXTRA CHUNK 89 88 1 1.12 
LONG (ADV AFTER) 77 81 -4 -5.19 
SHORT (ADV AFTER) 67 65 2 2.99 
MISSING CHUNK 50 54 -4 -8.00 
SHORT (ADV BEFORE) 53 48 5 9.43 
EXTRA 	
 TOK 47 47 0 0.00 
Table 7.  Effect of Number and Construct informa-
tion on most frequent error classes 
The error classes most affected by the number 
and construct information were split and merge – 
WPNC has a tendency of splitting chunks, which 
resulted in some unjustified splits, but compen-
sates this by fixing over a third of the merging 
mistakes. This result makes sense – construct and 
local agreement information can aid in the identi-
fication of predicate boundaries.  This confirms 
our original intuition that morphological features 
do help in identifying boundaries of NP chunks. 
6 Conclusion and Future work 
We have noted that due to syntactic features such 
as smixut, the traditional definition of base NP 
chunks does not translate well to Hebrew and 
probably to other Semitic languages. We defined 
the notion of Simple NP chunks instead. We 
have presented a method for identifying Hebrew 
Simple NPs by supervised learning using SVM, 
providing another evidence for the suitability of 
SVM to chunk identification.  
We have also shown that using morphological 
features enhances chunking accuracy. However, 
the set of morphological features used should be 
chosen with care, as some features actually hurt 
performance. 
Like in the case of English, a large part of the 
errors were caused by conjunctions – this prob-
lem clearly requires more than local knowledge.  
We plan to address this issue in future work. 
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