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{{Paper
|id=None
|storemode=property
|title=A Linguistic Method into Stemming of Arabic for Data Compression
|pdfUrl=https://ceur-ws.org/Vol-971/poster2.pdf
|volume=Vol-971
|dblpUrl=https://dblp.org/rec/conf/dateso/SooriPS13
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==A Linguistic Method into Stemming of Arabic for Data Compression==
https://ceur-ws.org/Vol-971/poster2.pdf
A Linguistic Method into Stemming of Arabic
A Linguistic Method into Compression
for Data Stemming of Arabic for Data
Compression
Hussein Soori, Jan Platoš, and Václav Snášel
Hussein Soori, Jan Platos, Vaclav Snasel
Faculty of Electrical Engineering and Computer Science
Faculty of Electrical
VSB-Technical Engendering
University and Computer
of Ostrava, Science,
Czech Republic
VSB-Technicaljan.platos,vaclav.snasel}@vsb.cz
{sen.soori, University of Ostrava, Czech Republic
{sen.soori, jan.platos,vaclav.snasel}@vsb.cz
Abstract. Creating good stemming rules for the Arabic language comes from
the importance of Arabic language as the sixth most used language in the word.
Stemming is very important in information retrieval, data mining and language
processing. With Arabic having complex morphology and grammatical proper-
ties, this poses a challenge for researchers in this field. In this paper, we try to
use an online morphological parser to distinguish parts of speech (POS), and
then set some extracting rules to produce stems, and finally, mismatch these
stems with an electronic dictionary. As a pilot study for this method, in this pa-
per we deal with three POS: nouns, verbs and adjectives.
Keywords: Stanford Online Parser, data compression for Arabic, Arabic natu-
ral language processing, Arabic data mining, Arabic morphology, stemming of
Arabic.
1. Introduction
The rapidly growing number of computer and Internet users in the Arab world and the
fact that the Arabic language is the sixth most used language in the world today cre-
ates a demand for more research in the area of data mining and natural language pro-
cessing in Arabic language. Another two factors maybe that Arabic alphabet is the
second-most widely used alphabet around the world - Arabic script has been used and
adapted to such diverse languages as Amazigh (Berber), Hausa, and Mandinka (in
West Africa), Hebrew, Malay (Jawi in Malaysi and Indonesia), Persian, the Slavic
tongues (also known as Slavic languages), Spanish, Sudanese, and some other lan-
guages, Swahili (in East Africa), Turkish, Urdu [10], and that Arabic is one of the six
languages used in the United Nations [11] after the Latin alphabet.
1.1 Arabic Complex Morphological and Grammatical Properties
A few challenges may face researchers as for as the special nature of Arabic script is
concerned. Arabic is considered as one of the highly inflectional languages with com-
plex morphology. Unlike most other languages, it is written horizontally from right to
left. It consists of 28 main letters. The shape of each letter depends on its position in a
V. Snášel, K. Richta, J. Pokorný (Eds.): Dateso 2013, pp. 119–128, ISBN 978-80-248-2968-5.
120 Hussein Soori, Jan Platoš, Václav Snášel
word—initial, medial, and final. There is a fourth form of the letter when written
alone. One example of this can be given for the letter ( )عas follow:
Initial Medial Final Separate
عـ ـعـ ـع ع
Fig. 1. Arabic Alphabets
Moreover, the letters alif, waw, and ya (standing for glottal stop, w, and y, respective-
ly) are used to represent the long vowels a, u, and i. This is very much different from
Roman alphabet which is naturally not linked. Other orthographic challenges can be
the the persistent and widespread variation in the spelling of letters such as hamza ()ء
and ta’ marbuTa ( ) ة, as well as, the increasing lack of differentiation between word-
final ya ( ) يand alif maqSura ( ) ى. Typists often neglect to insert a space after
words that end with a non-connector letter such as و, ز, [ ر3]. In addition to that,
Arabic has eight short vowels and diacritics (َ , , ٌ , ٍ ,ً , ْ , ُ , ِ ). Typ-
ists normally ignore putting them in a text, but in case of texts where typists do put
them, they are pre-normalized –in value- to avoid any mismatching with the diction-
ary or corpus in light stemming. As a result, the letters in the decompressed text, ap-
pear without these special diacritics.
Diacritization has always been a problem for researches. According to Habash [12],
since diacritical problems in Arabic occur so infrequently, they are removed from the
text by most researchers. Other text recognition studies in Arabic include, Andrew
Gillies et al. [11], John Trenkle et al. [30] and Maamouri et al. [20].
Other than letters, another factor determain the word identity and in many instances
can change the meaning and part of speech. This factor is the eight short vowels and
diacritics (ِ , ِ , ُِ , ِْ , ًِ , ٍِ , ٌِ , ِ ). An example for ( )رجلis
given in the following table where we can see the total change in word category and
meaning as a result of adding the diactricals which resulted in producing three differ-
ent words in meaning and three different parts of speech for the same three letter رجل
:
Word Meaning Part of Speech
ر ُج ُل man noun (subject)
رجُل man noun (object)
رجْ ل foot noun
رجل to go on foot (rather than, verb
e. g., ride a bike)
Never the less, it is always advised that these vowels and diacritics are often normal-
ized before processing in most light stemming or morphological approaches [4].
Mainly the reasons for not including them in the word processing is the claim that
they do occur so infrequently, and that in Modern Standard Arabic (MSA), people
A Linguistic Method into Stemming of Arabic for Data Compression 121
tend not to use them and, as a result of that, the meaning is left for the native speak-
er’s intuition, or , in some cases, can be determined from the context. This problem is
still waiting for a challenging attempt where the processor is ready to process words
with or without diacritics, without needing to normalize words.
Another morphological feature in Arabic is that, unlike Roman letters which are sepa-
rated naturally, Arabic has an agglutinated nature(as mentioned above) where letters
are linked to each other in some cases, while unlinked in some other case, depending
on position of the letter in the root, stem and word level. For example, in English the
pronoun (he) in (he plays) is separated from the following noun (plays), while in Ara-
bic the pronoun is represented by the letter ( )يwhich is linked to the root verb لعبto
form ( يلعبhe plays). The same is true when it comes to different kinds of Affixes.
Arabic has four types of affixes. Prefixes: these are letters (normally one) that change
the tense of the verb from past to present, such as the letter ( )يin case of the verb لعب
and يلعبabove. Suffixes: these represent the inflectional terminations (endings) of
verbs, as well as, the female and dual/plural markers for the nouns. Postfixes: these
are the pronouns attached at the end of the word. Antefixes: these are prepositions
agglutinated to the beginning of words.
1.2 The Problem at Hand:
This paper is trying to improve the rules for stemming of Arabic texts for data com-
pression. A few different linguistic methods were used by us in the past, for example:
the vowel letter method [2]. This method was mainly dependent on syllabification of
words and focused on splitting words according to vowel letters. The second approach
[8] was a simple approach into stemming rules, where 4 category of words were se-
lected (nouns, verbs, adjectives and adverbs) from short news item texts. These two
approaches produced some good results. However, two major problems showed up.
The first problem had to do with parts of speech (POS) recognition problem. For ex-
ample, the verb ( يلعبplays) starts with the letter ()ي. In Arabic, adding the suffix ()ي
is a very common way to change the word from its past form into its present form.
When some rules are set to remove the letter ( )يso to produce the root form of لعب,
these rules always removed the letter ( )يfrom other POS as well, such as the word
( يمنYemen) where the letter ( )يis part of the root word .
The second problem occurs within the sub-POSs when, for example, trying to remove
the determiner ( الthe definite article 'the') from common nouns as in ( الطالبthe stu-
dent). The rules set remove the الfrom all nouns including proper nouns such as,
( المانياGermany) where the الis part of the original noun and not a determiner.
For these reasons, in this paper we try to use Stanford online [9 ] to better categorize
the different POS and later to be mismatch the output words -after stemming- with an
elctronic dictionary.
1.3 The Stanford Online Parser
The Stanford parser is a powerful online parser that parses texts in three languages:
Arabic, Chinese and English. This parser is using dependency grammar. The Arabic
parts of the parser [9]is depending on the Penn Treebank project that was launches in
122 Hussein Soori, Jan Platoš, Václav Snášel
2001 in the University of Pennsylvania and headed by Prof. Mohamed Maamouri.
According to this corpus documentation [10], this corpus is designed for those who
study or use languages professionally or academically, as well as, for those who need
text corpora in their work. The Penn Arabic Treebank is particularly suitable for lan-
guage developers, computational linguists and computer scientists who are interested
in various aspects of natural language processing.
Table 1: English transliteration of Arabic alphabets
Arabic Alphabet Transliteration Arabic Alphabet Transliteration
ا alif ع Ayn
ب baa غ ghayn
ت ta ف faa
ث tha ق qaaf
ج jiim ك kaaf
ح haa ل laam
خ kha م miim
د daal ن nuun
ذ thal هـ haa
ر raa ة taMarboota
ز zay و waaw
س siin ال laamAlif
ش shiin ء hamza
ص Saad ئ hamzaONyaa
ض Daad ؤ hamzaONwaaw
ط Taa ي yaa
ظ Dhaa ى alifMaqsoora
A Linguistic Method into Stemming of Arabic for Data Compression 123
1.4 The Arabic Alphabets Transliteration System
In this study, we use a transliteration system for Arabic Alphabets so to enable non-
Arabic speakers identify Arabic alphabets and to to understand the rules proposed. A
legend of Arabic Alphabets and their English transliterations is provided in Table 1.
2. Stemming Rules
According to Stanford Online Parser for Arabic language, there are 27 different POSs.
In this paper, a number of rules are set for 3 main POSs: nouns, verbs and adjectives
as follows:
The rule for every POS or sub-POS is divided into steps as shown below. Every step
is to be implemented in the order of numbering:
Specifications
W – any word or its part (word referes to any POS in the rule: noun, verb, adjective,
etc.)
[] – arabic letter
Ins(x, y) – return true when x is anywhere in y
|x| - length of word x
[x]W – letter x is at the beginning of the word
Nouns Rules:
a) DTNN: determiner + singular common noun
Step 1: [alif laamAlif laamAlif]W -> [alif laam]W
Step 2: [alif laamAlif]Wxy -> [alif laam]Wy
b) DTNNP: determiner + singular proper noun
Step 1: [alif laam]W -> W
c) DTNNS: determiner + plural common noun
Step 1: [alif laam]W -> W
d) NNPS: common noun, plural or dual
Step 1: W[ta] -> W
W[yaa nuun] -> W
Step 2: |W| < 5 -> W[taMarboota]
Step 3: W[waaw][taMarboota] -> W[taMarboota]
124 Hussein Soori, Jan Platoš, Václav Snášel
Verbs Rules:
a) VBD: perfect verb (***nb: perfect rather than past tense)
Step 1: |[waaw]W|>2 -> W
Step 2: W[alif] -> W
W[ta] -> W
W[waaw nuun] -> W
Step 3: W[alif haa] -> W[alifMaqsoora]
W[ta haa] -> W[alifMaqsoora]
b) VBN: passive verb (***nb: passive rather than past participle)
Step 1: [yaa]W -> W
Step 2: |W| = 4 & [ta]W -> [alif]W
c) VBP: imperfect verb (***nb: imperfect rather than present tense)
Step 1: [ta]W -> W
[ta ta]W -> W
[yaa]W -> W
Step 2: W[waaw] -> W
Step 3: [nuun]W -> W
[waaw nuun]W -> W
[haa]W -> W
[haa alif]W -> W
Step 4: |W| = 2 -> W[alifMaqsoora]
Step 5: W[yaa] -> [alif]W[alifMaqsoora]
Step 6: [siin]W & ins(W, [ta]) -> [alif][siin]W
Step 7: W[waaw laam] -> W[alif laam]
W[waaw laam waaw nuun] -> W[alif laam]
W[waaw nuun] -> W[alif laam]
Step 8: [nuun][ta]W & |[nuun][ta]W| > 3 -> [nuun]W
Adjectives Rules:
a) DTJJ: determiner + adjective
Step 1: [alif laam]W -> W
Step 2: W[taMarboota] -> W
3. Experiments
The suggested rules must be tested against real data. For this purpose, we use some
news articles, from the BBC Arabic and Al Jazeera Arabic news portals. These arti-
cles are parsed by Stanford Online Parser and the results are shown in table 2. In the
A Linguistic Method into Stemming of Arabic for Data Compression 125
following table, repeated words are deleted and sample words of every POS or sub-
POS are shown in the table.
Table 2. List of words used in our experiments
Nouns
a) DTNN: determiner + singular common noun
البحر البنزين البيئة الجرف الحد الدفاع السيناتورين الشرق
المحيط النفط الوصول الشؤون البر المصادر السابق الحكوم
العالم االحتياطي التنقيب العام الدولة االبار االحتياطي االمر
االمور االميال االنسان بالكالب الطاقة الطلب العواصف المثبت المحيط
المشاغل الموارد المياه النفط الوحل االنواع البيئة العاصمة االخطار
الفصائل البيئة الدردشةالسهل
b) DTNNP: determiner + singular proper noun
االنترنت البرازيل الدوحة العاج المكسيك
c) DTNNS: determiner + plural common noun
االمريكيين االولويات الجمهوريون الديموقراطيون الشركات العشرات
لتحقيق للحماية المحافظون المشترين المعامالت الملوثات المنتجات
المندوبون المنصات الواليات
d) NNPS: common noun, plural or dual
تغييرات تقديرات جماعات سنوات طبقات طنين عشرات عمليات كميات
مجتمعات
مقترحات منصات
Verbs
)a) VBD: perfect verb (***nb: perfect rather than past tense
اصبح اصبحت اقترع ايد بامكاننا اجراها اجرته ادى استخرجت
مضى وتزيد وقال وقد وقدمت ويضيفون تسبب تسربا جعلت فقد كان
ويقول
)b) VBN: passive verb (***nb: passive rather than past participle
يوجد تسحب يرجح تستخدم يدرج
)c) VBP: imperfect verb (***nb: imperfect rather than present tense
تبدو تتنافس تتوزع تتوقف تتيحها تحتوي تربض تستخرج تسجل
تشارك تشير تصبح تطا تطفو تعد تالحظه تنتج تهدد يبدو يبلغ
يتعرض يتفوق يتم يتناولها يحاولون يحتوي يختبئى يخرجها
يقع يقول يقترع يزال يساهم يستحق يستفيد يسمون يشكل يصبح
يمكن ينتشر ينتهي ينجح يهدد يقولون يكاد يكون يمارس يمثل
Adjectives
a) DTJJ: determiner + adjective
126 Hussein Soori, Jan Platoš, Václav Snášel
االستقاللية االشعاعية االمريكي االمريكية االولى البرية البيئية
التجارية الجاري الجديد الحمراء الحيوانية الخارجي الداخلية
القاري القانونية الدولي الدوليةالطبيعية العالمي العالمية
المرجانية القطبية القطرية المتبقية المتحدة المحلية المحمية
المهددة المهيمن
النادرة النفطية الواسعة
Before any rule is applied, all words must be normalized and preprocessed. We store
all words in plain text files using codepage 1256 – Arabic. Because all our software is
written in C+, we read these text files into Unicode representation.
Our results for the nouns list are depicted in Tables 3, 4, 5 and 6. The results for the
noun rules produced very good results in case of DTNNP and DTNN. Very few un-
desirable results were produced because some words were wrongly parsed by the
parser such as ()بالكالب. As for DTNNS, some more rules needed to deal with the
plural and dual suffixes. NNPS produced very good results.
Table 3. Processed Nouns - DTNN: determiner + singular common noun
حكوم سابق مصادر بر شؤون شرق سيناتور دفاع حد جرف بيئة بنزين بحر
طاقة بالكالب مر ميل البر دولة عام تنقيب عالم وصول نفط محيط
عاصمة بيئة النوع وحل نفط مياه موارد مشاغل محيط مثبت عواصف طلب
دردشة سهل بيئة فصائل الخطر
Table 4. Processed nouns - DTNNP: determiner + singular proper noun
عاج مكسيك دوحة برازيل انترنت
Table 5. Processed nouns - DTNNS determiner + plural common noun
لتحقيق عشرات شركات ديموقراطيون جمهوريون اولويات امريكيين
منصات واليات مندوبون منتجات ملوثات معامالت مشترين محافظون للحماية
Table 5. Processed Nouns -NNPS: common noun, plural or dual
مقترح منصة مجتمع كمية عملية عشرة طنة طبقة سنة جماعة تقدير تغيير
The verbs' rules results are depicted in Tables 7, 8 and 9. The verbs' rules produced
good results in case of VBD and VBN. However, in case of VNP, a few bad results
show up and the rules have to be enhanced in the future.
Table 7. Processed Verb - VBD: perfect verb
جعلت تسرب تسبب بامكانن ايد اقترع اصبح اصبح استخرج ادى اجرى اجرى
يضيف يقول قدمت قد قال تزيد مضى كان فقد
A Linguistic Method into Stemming of Arabic for Data Compression 127
Table 8. Processed verbs – VBN: passive verb
وجد سحب درج استخدم رجح
Table 9. Processed Verb – VNP: imperfect verb
طاى اصبح شارك سجل استخرج ربض احتوى تيحها توقف توزع تنافس بدى
ختبئى احتوى حاال تناولها تمى تفوق تعرض بلغ بدى تجى الحظ عدى طفى
كال كاد قال قال قعى اقترع اصبح شكل استفيد استحق ساهم زال خرجها
مكن مثل مارس
The results for the adjectives’ rules are depicted In Table 10. Almost all rules made
for adjectives produced successful results.
Table 10. Processed adjectives - DTJJ
جديد جاري تجاري بيئي بري اولى امريكي امريكي اشعاعي استقاللي
قاري عالمي عالمي طبيعي دولي دولي داخلي خارجي حيواني حمراء
نادر مهيمن مهدد مرجاني محمي محلي متحد متبقي قطري قطبي قانوني
نفطي واسع
4. Conclusion
In this paper we set rules for POS and to parse our training data, we used Stanford
Online Parser for Arabic language, which identifies 27 different POSs. In this paper,
the rules set are for 3 main POSs: nouns, verbs and adjectives. Every rule for every
POS or sub-POS is divided into one or more steps.
The results for the noun rules produced very good resuts in case of DTNNP and
DTNN. Very few undesirable results occur because some words were wrongly parsed
by the parser such as ()بالكالب. As for DTNNS, some more rules needed to deal
with the plural and dual suffixes. NNPS produced very good results. The verbs' rules
results are depicted in Tables 7, 8 and 9. The verbs' rules produced very good results
in case of VBD and VBN. However, in case of VNP, a few bad results show up and
the rules have to be enhanced in the future. The results for the adjectives's rules are
depicted In Table 10. Almost all rules made for adjectives produced very good results.
Most errors occurred in case of VBP. However, the overall evaluation of these rules
proved that the rules produced very good results. In the future, these rules must be
improved and enhanced to include more POSs and should be tested against wider
variety of vocabulary and bigger corpora.
Acknowledgments: This work was partially supported by the Grant Agency of the
Czech Republic under grant no. P202/11/P142, SGS in VSB – Technical University
of Ostrava, Czech Republic, under the grant No. SP2013/70, and has been elaborated
in the framework of the IT4Innovations Centre of Excellence project, reg. no.
CZ.1.05/1.1.00/02.0070 supported by Operational Programme 'Research and Devel-
opment for Innovations' funded by Structural Funds of the European Union and state
128 Hussein Soori, Jan Platoš, Václav Snášel
budget of the Czech Republic and by the Bio-Inspired Methods: research, develop-
ment and knowledge transfer project, reg. no. CZ.1.07/2.3.00/20.0073 funded by Op-
erational Programme Education for Competitiveness, co-financed by ESF and state
budget of the Czech Republic.
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