Identifying Broken Plurals in Unvowelised Arabic Text 
 
Abduelbaset Goweder 
University of Essex 
Dept. of Computer 
Science 
Wivenhoe Park,  
Colchester  CO4 3SQ, 
UK 
agowed@essex.ac.uk 
Massimo Poesio 
University of Essex 
Dept. of Computer 
Science 
Wivenhoe Park, 
Colchester  CO4 3SQ, 
UK 
poesio@essex.ac.uk 
Anne De Roeck 
The Open University 
Dept. of Computing 
Walton Hall, Milton 
Keynes 
Buckinghamshire, MK7 
6AA, UK 
A.DeRoeck@open.ac.uk 
Jeff Reynolds 
University of Essex 
Dept. of Computer 
Science 
Wivenhoe Park, 
Colchester  CO4 3SQ, 
UK 
reynt@essex.ac.uk 
 
Abstract 
Irregular (so-called broken) plural identification 
in modern standard Arabic is a problematic issue 
for information retrieval (IR) and language 
engineering applications, but their effect on the 
performance of IR has never been examined. 
Broken plurals (BPs) are formed by altering the 
singular (as in English: tooth  teeth) through 
an application of interdigitating patterns on 
stems, and singular words cannot be recovered 
by standard affix stripping stemming techniques. 
We developed several methods for BP detection, 
and evaluated them using an unseen test set. We 
incorporated the BP detection component into a 
new light-stemming algorithm that conflates both 
regular and broken plurals with their singular 
forms. We also evaluated the new light-stemming 
algorithm within the context of information 
retrieval, comparing its performance with other 
stemming algorithms. 
1. Introduction 
Broken plurals constitute ~10% of texts in large 
Arabic corpora (Goweder and De Roeck, 2001), and 
~41% of plurals (Boudelaa and Gaskell, 2002). 
Detecting broken plurals is therefore an important 
issue for light-stemming algorithms developed for 
applications such as information retrieval, yet the 
effect of broken plural identification on the 
performance of information retrieval systems has 
not been examined. We present several methods for 
BP detection, and evaluate them using an unseen 
test set containing 187,309 words. We also 
developed a new light-stemming algorithm 
incorporating a BP recognition component, and 
evaluated it within an information retrieval context, 
comparing its performance with other stemming 
algorithms.  
We give a brief overview of Arabic in Section 2. 
Several approaches to BP detection are discussed in 
Section 3, and their evaluation in Section 4. In 
Section 5, we present an improved light stemmer 
and its evaluation. Finally in Section 6, our 
conclusions are summarised.  
2. Arabic Morphology and its Number 
System 
Arabic is a heavily inflected language. Its 
grammatical system is traditionally described in 
terms of a root-and-pattern structure, with about 
10,000 roots (Ali, 1988). Roots such as drs () 
and ktb () are listed alphabetically in standard 
Arabic dictionaries like the Wehr-Cowan (Beesley, 
1996). The root is the most basic verb form. Roots 
are categorized into: triliteral, quadriliteral, or rarely 
pentaliteral. Most words are derived from a finite set 
of roots formed by adding diacritics1 or affixes 
(prefixes, suffixes, and infixes) through an 
application of fixed patterns which are templates to 
help in deriving inflectional and derivational forms 
of a word.  Theoretically, several hundreds of 
Arabic words can be derived from a single root. 
Traditional Arab grammarians describe Arabic 
morphology in terms of patterns associated with the 
basic root f3l (	, “to do”)- where f, 3, and l are like 
wildcards in regular expressions: the letter f (
 
,“pronounced fa”) represents the first consonant 
(sometimes called a radical), the letter 3 ( , 
“pronounced ain”) represents the second, and the 
letter l ( , “pronounced lam”) represents the third 
                                                                
1 Special characters which are superscript or subscript marks 
added to the word. 
respectively. Adding affixes to the basic root f3l 
(	, “to do”) allows additional such patterns to be 
formed. For instance, adding the letter Alef () as a 
prefix to the basic root f3l (	, “to do”) we get the 
pattern Af3l (	) which is used to form words such 
as: anhr (, “rivers”), arjl (, “legs”), and asqf 
(, “ceilings”). Some examples of the word 
patterns are Yf3l (), Mf3Wl (), Af3Al (	), 
MfA3l (), etc.  
The following example shows how we can use 
patterns to form words. the verb yktb ( , “he 
writes or he is writing”) is formed by mapping the 
consonants of the triliteral root ktb ( ) to the 
pattern yf3l (), where the letters f (
), 3 (), and 
l () in the second, third, and fourth positions of the 
pattern respectively represent slots for a root 
consonant. Figure 1 depicts the process of matching 
the root ktb ( ) to the pattern yf3l () to 
produce the verb yktb ( , “he writes or he is 
writing”), then adding prefixes and/or suffixes to 
obtain a word. 
 
Figure 1: The process of mapping the root ktb (  ) to the 
pattern yf3l (). 
 
The Arabic number system has singular, dual, and 
plural. Plurals are traditionally distinguished into 
two categories: the regular (so-called sound) 
plurals, and the irregular (so-called broken) plurals. 
Sound Plurals are formed by appropriate suffixation 
(like English: hand  hands). The sound masculine 
plural is formed by adding the suffix oun (!") in the 
nominative case and the suffix een (#) in the 
accusative & genitive cases. The sound feminine 
plural is formed by attaching the suffix at () to the 
singular.  
Irregular or broken plurals apply mostly to triliteral 
roots and are formed by altering the singular (as in 
English: tooth  teeth). Many nouns and adjectives 
have broken plurals (Haywood and Nahmad, 1976). 
In all cases, singulars are affected by applying a 
number of different patterns that alter long vowels 
(Alef (), Waw ("), Yeh ($), and Alef-Maqsura (%)), 
within or outside the framework of the consonants 
(Cowan, 1958). Table 1 gives some examples of 
BPs and their patterns.  
Table 1: Broken Plural examples. 
Singular BP Pattern Plural 
qlm(&'(, “pen”) Af3Al (	) AqlAm()*(, “pens”) 
qlb('(, “heart”) f3Wl (	) qlWb('(, “hearts”) 
ktab(, “book”) f3l (	) ktb(, “books”) 
 
The complexity of Arabic morphology has 
motivated a great deal of studies. Some of which 
especially concerned with broken plurals (McCarthy 
and Prince, 1990b; Kiraz, 1996a; Idrissi, 1997). 
These are successful to varying degrees, but have a 
main practical drawback in the context of 
information retrieval: they assume that words are 
fully vowelised. Unfortunately, short vowels are 
usually not written in published Arabic text, with 
the exception of the religious texts (e.g., the Holy 
Quran), poetry, and books for school children 
(Abuleil and Evens, 1998). 
3.  Different Approaches to BP Identification 
We tested several different approaches for BP 
identification: simple BP matching, restricted BP 
matching (hand restricted & decision tree 
restricted), and a dictionary approach. 
3.1 The Simple BP Matching Approach 
Given the characterisation of broken plurals one 
finds in standard Arabic grammars, the most 
obvious method for identifying a broken plural is to 
light stem it (strip off any prefixes and/or suffixes), 
then trying to match the obtained stem against BP 
patterns found in standard grammars. Since this 
method is widely used, we adopted it as a baseline. 
As a first step towards a simple BP matching 
algorithm, we developed a basic light stemmer for 
Arabic by modifying an existing root stemmer 
(Khoja and Garside, 1999). This basic light stemmer 
was incorporated in a simple BP identification 
module based on matching, using a list of 39 BP 
patterns found in grammar books such as Haywood 
and Nahmad (1976) and Cowan (1958). The simple 
BP matching algorithm takes in a word; light-stems 
it to produce morphological information such as 
stem, prefix and suffix; and returns TRUE if the 
stem matches one of the BP patterns in the list. The 
stem matches a BP pattern if and only if they have 
the same number of letters and the same letters in 
the same positions, excluding the consonants f (
), 
3 (), and l () of the basic root f3l (	, “to do”) 
found in the pattern.  
In information retrieval and statistical natural 
language processing, recall and precision are 
common measures used to gauge a system’s 
performance. Recall (R) is the fraction of target 
items that a system selected, while the precision (P) 
is the fraction of selected items that a system got 
right. A third measure known as F-measure (F)2 
(combines R and P) is used in some situation where 
R is very high and P is very low (Manning and 
Schutze, 1999). We implemented R, P, and F to 
evaluate approaches we present in this paper. 
The simple BP matching algorithm was 
preliminarily evaluated on a subset of an Arabic 
corpus (referred to as A_Corpus1) obtained from 
Khoja (1999). It contains 7172 words whose  BP 
instances were identified (this first set of BPs is  
referred below as data set1). The results showed that 
the simple BP matching approach has very high 
recall (99.71%), but low precision (13.73%).  
We also tested two slightly modified versions of the 
simple BP matching algorithm, exploiting 
information about affixes information and proper 
name detection, respectively. The first variant was 
based on the observation that only a limited set of 
prefixes and suffixes can be attached to a BP stem. 
In addition,  some BP prefixes and suffixes cannot 
both be concatenated to a BP stem at the same time. 
These observations led to a first variant of the 
simple matching algorithm incorporating two post-
processing strategies for refining the decisions made 
by the simple BP matching algorithm. The first 
refining strategy checks if the produced prefix or 
suffix is in the list of BP prefixes or suffixes; if it 
isn’t, the stem will be classified as ‘Not Broken 
Plural (NBP)’. The second refining strategy checks 
if the prefix is a definite article (e.g., al (, “the”), 
wal (", “and the”), bal (+, “with the”), etc.) and 
the suffix is a BP suffix, and changes the output 
accordingly. An evaluation of the performance of 
                                                                
2 F=2PR/(P+R) for equal weighting. 
the simple BP matching algorithm with affix-based 
refinement strategies on  data set1 revealed a slight 
improvement in precision (16.74%). 
We also made a preliminary test evaluating the 
possible usefulness of incorporating a proper name 
detector in the system.  We manually identified  the 
proper names in data set1, then modified the simple 
BP matching algorithm to ignore proper names.  Our 
results only showed a small (if significant)  
improvement in precision (19.86%), that we didn’t 
feel would justify the considerable effort required to 
develop a proper name detector. As a result,  we 
looked for simpler but more effective  ways to 
improve the algorithm. 
3.2 The Restricted BP Matching Approach 
The main problem with the simple BP matching 
approach is that the BP patterns are too general to 
achieve a good performance. Another way to 
improve the precision of the algorithm is therefore 
to obtain more specific BP patterns by restricting the 
original ones. The idea is to allow only a subset of 
the alphabet to be used in the meta characters f (
), 
3 (), and l () positions of the patterns (see Section 
2), producing a number of more restrictive patterns 
out of each original BP pattern. A larger number of 
instances of each BP pattern is required to develop 
this approach. For this purpose, we used a large 
corpus of ~18.5 million words (Goweder and De 
Roeck, 2001). In the remainder of the paper, we 
refer to this corpus as A_Corpus2. The simple BP 
matching algorithm with affix-based refinement 
strategies was used to extract all instances of BPs 
that occurred in A_Corpus2. We adopted two 
approaches. In a first experiment we tried to produce 
the more restrictive patterns by hand. Later we tried 
to achieve the same goal using a decision tree 
technique. We discuss the first experiment here, the 
second in section 3.4. The procedure we followed to 
identify the BPs in A_Corpus2 is as follows: 
1. A word frequencies tool was used to 
generate word frequencies for A_Corpus2, 
obtaining 444,761 distinct word types. 
2. Each word type was light-stemmed.  
3. The word frequencies tool was run again on 
the stemmed word types, producing roughly 
127,000 stem types. 
4. The 127,000 stem types were fed to the 
simple BP matching system to retrieve all 
stems that match BP patterns. The output 
file, categorised according to each BP 
pattern, contained about 30,000 cases. Each 
specific pattern contained a list of stems 
matching this pattern. 
We then studied separately each BP pattern. Some 
BP patterns were straightforward to restrict. For 
example, all the stem types matching the BP pattern 
Af3lAa (,*	), are shown in Figure 2. There are 107 
cases in total. An analysis of the results reveals that 
only 18 cases are BPs, highlighted (bold and 
underlined). In the BP pattern Af3lAa (,*	), the 
meta characters f (
), 3 (), and l () are in 
positions 2, 3, and 4 respectively. The remaining 
characters - Alef () in positions 1 & 5, and Hamza 
(,) in position 6 - are fixed. Our analysis showed 
that the stems which have the letter Ta () in the 3rd 
position are not BPs; they are nominalizations of 
verbs. For example, the word abtdaa (,-+ , 
“starting”) listed on the first row and last column is 
a noun derived from the verb yabtdi ( -./  , “he 
starts”). There are 62 cases of this type. An 
exceptional rule could be induced to handle nouns 
derived from verbs. The rule could be written as:  
	
		

 
 
Figure 2: Results of the pattern Af3lAa (	
). 
 
The simple BP matching algorithm was modified to 
use the manually restricted BP patterns. The 
performance of the manual restriction method was 
evaluated using the same data set used before, data 
set1. The results show that precision is noticeably 
improved, to 53.92%. Recall is improved as well, to 
100%. The improvement in both recall and precision 
caused a big increase in the F-measure, to roughly 
70%. These results suggested to us that attempting 
to restrict the BP patterns is worthwhile. In section 
3.4, we discuss attempts to find restrictions 
automatically, using decision tree methods. But the 
classification of all words in A_Corpus2 as BP or 
NBP also allowed us to bootstrap a dictionary-based 
approach. We discuss this next.  
3.3 The Dictionary Approach 
In information retrieval applications, “the most 
common measures of system performance are time 
and space. The shorter the response time, the 
smaller the space used, the better the system is 
considered to be” (Baeza-Yates and Ribeiro-Neto, 
1999). The fastest way to detect BPs is to use a 
look-up table which lists all BP stems.  
Considering some of the facts about Arabic, 
discussed in Section 2, it is quite clear that it will be 
fairly difficult to build look-up tables listing either 
BP stems or full words from language dictionaries. 
A_Corpus2, on the other hand - a large resource of 
modern, unvowelised, freeflowing Arabic text - 
provided a good foundation,  and after the 
development of the simple and restricted BP 
matching algorithms discussed in the previous 
sections, only minor additional effort was required 
for building such a table  (without such tools, 
collecting the table entries would have been 
prohibitively expensive). 
The dictionary was built as follows: 
1. The manually restricted BP matching 
system was run on the 127,000 stem types, 
extracted from A_Corpus2 (see section 3.2), 
to retrieve all types that match (restricted 
matching) BP patterns. The results were 
about 12,000 instances in total. 
2. We then went through these 12,000 
instances, identifying the actual BPs. A list 
of roughly 3,600 BP stems, alphabetically 
ordered and categorised according to each 
BP pattern, was extracted. 
List of all words retrieved by the pattern (,*	)===>  
,-+ ,+ ,0+ ,*+ ,1+
    , ,2
 ,* , ,34 ,54 ,64
 ,4 ,74 ,4 ,.'4 ,.8
 ,68 ,8 ,*8 , 
  ,+ ,9: ,-: ,6:
 ,;: ,: ,7: ,: ,
 ,< ,=< ,*< ,; ,
 ,0 , , ,* ,
 ,> ,  ,? ,?
   ,@A ,- ,*
 ,7 ,1  ,3B ,1B
  ,-	 ,	 ,(	 ,-(
 ,;( ,( ,1(  
 , ,5 , , 
 ,@ ,* ,1.   ,-
 ,6 , , ,7 ,
 ,1C ,D ,*D ,1E ,"2
 ,.5 ,; ,@ ,@ ,
 ,;   , ,E7 ,-=
 ,= ! 
! "! ,>'
 ,*< ,:F   
Total number of cases is 107 
3. The list was further revised in collaboration 
with a linguist, who is an Arabic native 
speaker. The revised list contained exactly 
3,580 BP stems.  
We implemented the dictionary approach using hash 
tables, in which search, insertion, and deletion 
operations can be done in constant time.   
Before carrying an extensive comparison of the 
dictionary approach to the previous approaches, its 
performance was first tested on the same data set 
already used to evaluate both simple and restricted 
BP matching approaches, data set1. The results of 
the evaluation show that precision significantly 
improves (to 81.18%), while recall is still perfect 
(100%). The F-measure recorded an increase 
(89.61%) due to the improvement in the precision. 
The results suggest that the dictionary approach 
outperforms both the simple and manually restricted 
BP matching approaches. 
3.4 Learning Restrictions Automatically 
Decision tree learning is one of the most widely 
used classification methods. The decision tree 
learning algorithm C4.5 developed by Quinlan 
(1993) was used to generate a set of rules in the 
form of a decision tree and decision rules (if-then 
statements). Because we are interested in how we 
could restrict the BP patterns, specifically restricting 
the meta characters of the BP patterns (Fa, Ain, and 
Lam), we chose them to be the attributes which 
describe our data. The outcome (class) of each case 
is given as BP or NBP. Figure 3 shows the classes 
and the name & description of each attribute. 
 
 
Figure 3: Set of attributes.  
 
Table 2 lists some examples of the BP pattern 
Af3lAa (,*	) to show how instances of the data can 
be described according to the set of proposed 
attributes and a classification for each instance.  
 
 
Table 2: Sample of examples. 
Set of Attributes Word 
Fa Ain Lam 
Class  
Asdqaa(,(-A , “friends”) G  H BP 
Abtdaa(,-+ , “starting”)    NBP 
Akhtbaa( , “hiding”) I   NBP 
Athryaa(,J , “wealthy”) K  $ BP 
 
Data balance was an issue to be dealt with before 
conducting decision tree experiments. For some BP 
patterns, the number of BP cases is much smaller 
than the number of NBP cases. In such a situation, 
we are required to have equal cases for each class 
(50% for BP and 50% for NBP) because C4.5 tends 
to classify all the cases as one class with some error 
rate if there are an insufficient, or small number of 
cases of one type compared to the other. Balancing 
the data was achieved by duplicating the infrequent 
cases until we have an equal number of cases for 
both classes. 
Training data are generated using the simple BP 
matching algorithm, on the text file containing 
127,000 stem types extracted from A_Corpus2 (see 
section 3.2). The simple BP matching algorithm 
listed all instances that match every particular BP 
pattern. So far, we have a list of instances, which are 
labeled as BP, for each BP pattern, however, many 
of the cases are not BPs. As a result, we need to 
revise automatically the classification of each case 
using the dictionary-based approach (discussed in 
section 3.3). After the revision, all the cases which 
are labeled as BPs by the simple BP matching 
algorithm will be corrected by the dictionary 
approach. At this stage, each BP pattern will have a 
list of BP and NBP cases. The BP system will check 
which class has fewer cases in order to duplicate 
them to achieve the balancing. Thirty nine output 
files, one for each BP pattern, were produced by the 
BP system.   
Test data for each BP pattern are also generated by 
invoking the BP system on a large unseen data set, 
containing 187,309 words (referred to as data set2) 
extracted from the Arabic Newswire corpus (a third 
corpus referred to as A_Corpus3, and totally 
BP, NBP. 
Fa: discrete (list of Arabic alaphabet). 
Ain: discrete (list of Arabic alaphabet). 
Lam: discrete (list of Arabic alaphabet). 
different from A_Corpus1 and A_Corpus2) in order 
to test the models produced by C4.5 system. 
We generated one classification model for each of 
the 39 (mutually exclusive) BP patterns, and 
examined their performance on unseen test cases. 
Each classification model was trained  on a dataset 
specific to that BP pattern and consisting of 10,000 
cases, 50% for each class. The classification models 
were then evaluated on 39 different test sets (one for 
each BP pattern). Most of the classifiers were able 
to achieve the task with very low error rates and 
high recall & precision. Some models performed the 
classification without any errors and had a very 
simple decision tree (e.g., the decision tree and set 
of rules produced for the BP pattern Af3lAa (,*	)). 
This implies that the results are promising; however, 
some classifiers had large decision trees and 
suffered from overfitting. 
A summary of recall and precision results for both 
decision trees and set of rules are drawn as 
histograms to give us a better insight of how each 
BP pattern performed as shown in Figures 4, 5, 6, 
and 7. The analysis of the results shows that most of 
the models (Figures 4&6), representing BP patterns, 
achieved high recall (except a few of them, such as 
patterns 16, 27, where the recall was low L 40%). 
On the other hand, some models (Figures 5&7) 
performed poorly (precision L 40%), such as 
patterns 4, 10, 16, 17, 21, and 28. The performance 
of all combined models achieved an overall recall 
and precision of approximately 95% and 75% 
respectively. 
 
Decision Trees
0
20
40
60
80
100
120
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39
BP patterns
R
e
c
a
l
l
Overall Recall =  95.56%
 
Figure 4: Recall of decision trees for all BP patterns.  
 
Decision Trees
0
20
40
60
80
100
120
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39
BP patterns
P
r
e
c
i
s
i
o
n
Overall precision =  75.59053%
 
Figure 5: Precision of decision trees for all BP patterns.  
 
Set of Rules
0
20
40
60
80
100
120
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39
BP patterns
R
e
c
a
l
l
Overall Recall =  95.91184%
 
Figure 6: Recall of set of rules for all BP patterns. 
 
Set of Rules
0
20
40
60
80
100
120
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39
BP patterns
P
r
e
c
i
s
i
o
n
Overall precision =  75.14395%
 
Figure 7: Precision of set of rules for all BP patterns. 
 
4. Comparing the Performance of the BP 
Identification Approaches 
The BP matching methods discussed in the previous 
section were evaluated on a larger unseen data set, 
data set2 (the same data set already used to generate 
test cases to evaluate the decision tree approach, see 
section 3.4). The BPs in this data set were tagged as 
follows: 
1. A modified version of the dictionary-based 
BP identification algorithm was run on data 
set2 to tag all the occurrences of BPs. 
2. We manually went through the output twice 
to revise any mistakes made by the BP 
identification algorithm. 
The evaluation results for  the different algorithms 
on data set2 are listed in Table 3. These results 
confirm that the simple BP matching approach 
performed poorly, the restricted BP matching 
approach improved the performance significantly, a 
more significant improvement achieved by the 
decision tree technique, and the dictionary approach 
outperformed all the approaches. The results also 
suggest that affix-based refinement strategies 
improved the performance of the simple matching, 
the restricted matching, and the dictionary 
algorithms. 
Table 3: An Evaluation of different BP identification 
algorithms using a large data set (data set2). 
Evaluation Criteria BP  
Ident. 
Method R P F 
SM 99.5% 13.8% 24.2% 
SMR 100% 14.5% 25.4% 
MRM 99% 49.7% 66.2% 
MRMR 100% 52% 68.4% 
Dic 98.8% 86.9% 92.5% 
DicR 100% 92.3% 96.0% 
DT 95.9% 75.1% 84.3% 
  
Acronyms: 
Simple Matching 
 SM 
Simple Matching 
with Refinement  
SMR 
Manually 
Restricted 
Matching  MRM 
Manually 
Restricted 
Matching with 
Refinement  
MRMR 
Dictionary  Dic 
Dictionary with 
Refinement  
DicR 
Decision Trees       
 DT 
 
5. An Improved Light-Stemmer and its 
Task-Based Evaluation 
The dictionary-based BP detector with restriction 
was included  in a revised version of the light 
stemmer described earlier (henceforth: Basic-
LStemmer). This revised stemmer (henceforth: BP-
LStemmer) first runs the Basic-LStemmer on a 
word, then invokes the (dictionary-based) BP 
detector. If the BP detector returns TRUE, the 
singular form of the word is output; otherwise, the 
output of the Basic-LStemmer.  
The BP-LStemmer was evaluated in an information 
retrieval task by developing a new indexing method,  
referred to as “stem+BP”. This new indexing 
method was compared with the three standard 
indexing methods (full word, root, and ‘basic’ stem). 
The Greenstone digital library, developed at the 
University of Waikato in New Zealand, was used as 
an information retrieval system for our experiment. 
A collection of 385 documents (7 different domains) 
and a set of 50 queries (plausible queries that we 
might use ourselves were created to search for 
particular information in different domains) with 
their relevance judgments, were used to evaluate the 
four indexing methods. 
The results (Figure 8) clearly indicate that the 
proposed “stem+BP” indexing method significantly 
outperforms the three standard indexing/stemming 
methods (p (1-tailed) < .01 both by the Sign test and 
the Wilcoxon signed-rank test). This suggests that 
stemming has a substantial effect on information 
retrieval for highly inflected languages such as 
Arabic, confirming the results obtained by Al-
Kharashi and Evens (1994), Hmeidi et al. (1997), 
Abu-Salem et al. (1999), Larkey and Connell 
(2001), and Larkey et al. (2002). 
 
Average Recall versus Precision Figures
0
10
20
30
40
50
60
70
80
90
100
10 20 30 40 50 60 70 80 90 100
Recall
P
r
e
c
i
s
i
o
n Stem+BP
Stem
Root
Full Word
 
Figure 8: The Average Recall vs. Precision Figures of the 
Four Indexing Methods for 50 Queries. 
 
6. Conclusion 
We discussed several different methods for BP 
identification: simple BP matching, affix-based 
simple BP matching, simple BP matching+POS, 
manually-and-DT restricted, and dictionary-based. 
Although the simplest methods had poor or 
mediocre results, they were used to bootstrap better 
performing methods.  
The baseline, the simple BP matching method, has a 
high recall but a low precision (~14%). We 
attempted to improve the performance of the BP 
identification algorithm by (i) using affix 
information, (ii) identifying proper names, and (iii) 
restricting the BP patterns. Having implemented the 
simple and restricted methods, and used them to 
analyse all the BPs in a large corpus (A_Corpus2), 
made a dictionary approach possible. All methods 
were evaluated on a larger data set of 187,000 
words. The results confirmed that the restricted 
method clearly improved the overall performance 
and the dictionary approach outperformed the other 
ones. 
We also developed a new light-stemming algorithm 
that conflates both regular and broken plurals with 
their singular forms. The new light-stemming 
algorithm was assessed in an information retrieval 
context, comparing its performance with other 
stemming algorithms. Our work provides evidence 
that identifying broken plurals results in an 
improved performance for information retrieval 
systems. We found that any form of stemming 
improves retrieval for Arabic; and that light-
stemming with broken plural recognition 
outperforms standard light-stemming, root-
stemming, and no form of stemming. 
7. Acknowledgments 
We would like to express our gratitude to Shereen 
Khoja for providing her root stemmer. We would 
also like to thank the Libyan Secretariat of 
Education for supporting this work. 

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