1. Automatic Part-of-Speech Tagging of Arabic Text
School of Computing
FACULTY OF ENGINEERING
Automatic Part-of-Speech Tagging of Arabic Text
العَنْوَنَةُ الآلِيَّةُ لِنُصُوصِ اللُّغَةِ العَرَبِيَّةِ
Majdi Sawalha
Supervisor
Dr. Eric Atwell

2. School of Computing Outline: Introduction Research focus and questions
FACULTY OF ENGINEERING
Outline:
Introduction
Research focus and questions
A word about Arabic Language
Arabic Language Corpora
Gold standard for evaluation
Arabic Morphological Analysers and Stemmers
Prior-Knowledge broad-lexical resource
Hybrid Part-of-Speech tagger of Arabic language

3. Introduction What is Part of Speech Tagging? What is a tag?
School of Computing
FACULTY OF ENGINEERING
What is Part of Speech Tagging?
What is a tag?
What is the tagsets?
Our Aim
How to widen the scope of Arabic Part-of-Speech tagging, to develop a system which can process Arabic text in wide range of formats, domains, and genres of both vowelized and non-vowelized text ?

4. Research focus and questions
School of Computing
FACULTY OF ENGINEERING
How to widen the scope of Arabic Part-of-Speech tagging, to develop a system which can process Arabic text in wide range of formats, domains, and genres of both vowelized and non-vowelized text ?
Research sub-questions:
Can richer lexical resources derived from dictionaries and grammar text books improve the coverage of morphological analysis for wider range of Arabic text formats, domains and genres?
How do we evaluate existing Part-of-Speech taggers and new Part-of-Speech tagger on a wider range of text formats, domains, genres, and vowelized and non-vowelized text?
How do I make the best reuse of existing tagger components and methods?

5. Introduction Tagging Applications
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Tagging Applications
A good tagger can serve as a preprocessor.
Large tagged text corpora are used as data for linguistic studies.
Information technology applications;
Text indexing and retrieval.
Speech processing.

6. A word about Arabic Language
School of Computing
FACULTY OF ENGINEERING
Arabic language linguists classify words in Arabic into three main categories.
Verbs: that word which denotes an action and has tense.
Nouns: name of a person, place, or object and does not have any tense.
Particles: that word of which cannot be understood without joining a noun or a verb or both.

7. Verb classifications A word about Arabic Language Verb الفعل
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Verb classifications
Verb
الفعل
Complete Verb
فعل تام
Incomplete Verb
فعل ناقص
Transitive Verb
فعل متعدِّ
Intransitive Verb
فعل لازم
Active Verb
فعل معلوم
Passive Verb
فعل مجهول
Verb
الفعل
Complete Verb
فعل تام
Incomplete Verb
فعل ناقص
Transitive Verb
فعل متعدِّ
Intransitive Verb
فعل لازم
Active Verb
فعل معلوم
Passive Verb
فعل مجهول
Verb
الفعل
Complete Verb
فعل تام
Incomplete Verb
فعل ناقص
Transitive Verb
فعل متعدِّ
Intransitive Verb
فعل لازم
Active Verb
فعل معلوم
Passive Verb
فعل مجهول
Verb
الفعل
Complete Verb
فعل تام
Incomplete Verb
فعل ناقص
Transitive Verb
فعل متعدِّ
Intransitive Verb
فعل لازم
Active Verb
فعل معلوم
Passive Verb
فعل مجهول
Verb
الفعل
Complete Verb
فعل تام
Incomplete Verb
فعل ناقص
Transitive Verb
فعل متعدِّ
Intransitive Verb
فعل لازم
Active Verb
فعل معلوم
Passive Verb
فعل مجهول
Verb
الفعل
Perfect / Past Verb
الفعل الماضي
Progress Verb
الفعل المضارع
Imperative Verb
فعل أمر

8. A word about Arabic Language
School of Computing
FACULTY OF ENGINEERING
Nouns
Arabic language linguists distinguish between 21 types of nouns
Verbal noun
Original noun
Pronoun
Personal noun
Demonstrative noun
Joining nouns
Interrogative noun
Conditional noun
Generalization nouns
Adverb
Present participle
Past participle
Adjective
Increased present participle.
Comparing and contrasting entities, the comparative and the superlative
Adverb of place
Adverb of time
Noun of instrument
Proper noun
Noun of genus
Ordinal number nouns
Verb noun
The five nouns

9. A word about Arabic Language
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FACULTY OF ENGINEERING
Particles
Particles
Meaning Particles
Building Particles
Inactive Particles
Active Particles
Effects
Verb
Jussive
Subjunctive
Partial subjunctive
Noun
Genitive Case
Vocative
Exception
Both
Conjunction

10. Arabic Language Tagset
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FACULTY OF ENGINEERING
Evaluating existing Arabic tagsets.
Every researcher has developed a tagset. Either detailed or minimal tagset.
A comparison of different tagsets will show
The number of tags used,
The purpose of using the tagset.
The source of information when designing the tagset.
The errors in classifying tags into their categories.
Designing a more reliable and multi-level tagset that varies from minimal tagset to more detailed one.

11. A word about Arabic Language
School of Computing
FACULTY OF ENGINEERING
Arabic Language challenges
Writing constraints lead to ambiguities.
Tokenization.
Agglutination.
Complex Morphology.
Vowel Marks.
Grammatical ambiguity
2.8 in vowelized text and 5.6 in non-vowelized text

12. Tokenization What is a token?
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What is a token?
Main tokens are delimited by a white space or a punctuation mark
( ، ؟ ؛ ! . etc) .
Arabic Morphology allows words to be prefixed or suffixed with clitics.
Clitics can be concatenated one after the other.
Arabic clitics are not as easily recognizable.
A single word can comprise up to four independent morphemes.
Tokenizer is responsible for:
Defining word boundaries.
Demarcating clitics, multiword expressions, abbreviations and numbers.
Affixes carry morpho-syntactic features
- Tense - Person - Gender - Number)
Clitics serve syntactic functions
- Negation -Definition – Conjunction - Preposition

13. وَ لِ يَ كْتُبُ وُنَ هَا Tokenization Tokenization
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FACULTY OF ENGINEERING
Most Arabic words consist of stem/root and a combination of prefixes and suffixes.
Root
2- Prefix(es) + Root
Root + Suffix(es)
4- Prefix(es) + Root + Suffix(es)
Stem
6- Prefix(es) + Stem
Stem + Suffix(es)
8- Prefix(es) + Stem + Suffix(es)
كتب
يكتب
كتبه
يكتبه
كتاب
الكتاب
كتابهم
وكتابهم
ktb
yktb
ktbh
yktbh
ktAb
AlktAb
ktAbhm
wktAbhm
Wrote
Write
Wrote it
Writing it
Book
The book
Their book
And their book
وَلِـيَـــكْـتُـبُــوُنَـهَـا [ wlyktbwnhA ] (And they write it)
وَ * لِ * يَ * كْتُبُ * وُنَ * هَا (w*l*y*ktb*wn*hA) وَ لِ يَ
كْتُبُ
وُنَ
هَا
Conjunction
preposition
Progressive letter
Root
Relative Pronoun
(Plural/Subject)
(Object)

14. Vowels & Diacritical marks
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Arabic has 2 types of vowels
1- Long vowels: Alif ا , waw و , yaa ي (part of Arabic letters)
2- Short vowels: there small vowel marks which are not part of Arabic letters. These marks are placed above and below the Arabic letters.
Arabic has other 5 diacritical marks
Nunation is the doubling of the short vowels used at the end of indefinite nouns
Sukun (absence of a vowel) consonant is not followed by a vowel.
Gemination (Shadda) duplication of the consonant

15. Vowelization & Part-of-Speech Tagging
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FACULTY OF ENGINEERING
Importance of using diacritics in Arabic language
Adding semantic information to the words
Determining the correct tag to the word in the sentence
Indicating grammatical functions to the word
(Mood, Aspect, Voice endings for verbs, Case endings for nouns).
Indicating the correct pronunciation of word, correct syntactical analysis and removing the semantic confusion of Arabic readers.

16. Vowelization & Part-of-Speech Tagging
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Diacritical marks affect the Part-of-Speech tag of the word and its meaning

17. Corpora or (Corpuses) Corpus Applications of Corpora
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FACULTY OF ENGINEERING
Corpus
A collection of samples of texts that are selected and ordered according to explicit linguistic criteria in order to be used as a sample of the language.
Applications of Corpora
Prepare and format text to be used by search tools.
Useful for linguist, teacher and learner. (advanced level)
The study of syntactic structure.
Corpus in lexicography used for developing good dictionaries.
Used to train Machine Learning software for grammar analysis, word clustering, machine translation, …

18. Arabic Language Corpora
School of Computing
FACULTY OF ENGINEERING
Corpus of Contemporary Arabic (CCA) [University of Leeds Corpus] (2004)
Engineered by Latifa Al-Sulaiti & Eric Atwell; Written and some spoken; Around 1M words; TAFL; Websites and online magazines
FREE to download:
Buckwalter Arabic Corpus
Written; 2.5 to 3 billion words, Lexicography;Public resources on the Web
An-Nahar Corpus (2001)
Written;140M words; General research;
An-Nahar newspaper (Lebanon)
Al-Hayat Corpus (2002)
Written;18.6M words; Language Engineering and Information Retrieval; Al-Hayat newspaper (Lebanon)
Arabic Gigaword (2002)
Written; Around 400M words; Natual language processing, information retrieval, language modelling; Agence France Presse, Al-Hayat news agency, An-Nahar news agency, Xinhua news agency

19. Gold Standard Evaluation Corpus
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Building Gold Standard Evaluation Corpus
Different text domains, formats and genres of both vowelised and non-vowelised text.
The Qur’an.
Newspaper text.
Magazines.
School books.
Children’s books.
Blogs (text in blogs can be in Arabic script or in roman letters transcription)
Gold Standard will be checked by Arabic language scholars.

20. Gold Standard Evaluation Corpus
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FACULTY OF ENGINEERING
Sample of Qur’an Gold Standard (vowelized)
Sample of Newspaper Gold Standard (non-vowelized)
Alif. Lam. Mim. Do men imagine that they will be left (at ease) because they say, We believe, and will not be tested with affliction? Lo! We tested those who were before them. Thus Allah knoweth those who are sincere, and knoweth those who feign. Or do those who do ill-deeds imagine that they can outstrip Us? Evil (for them) is that which they decide. Whoso looketh forward to the meeting with Allah (let him know that) Allah's reckoning is surely nigh, and He is the Hearer, the Knower. And whosoever striveth, striveth only for himself, for lo! Allah is altogether Independent of (His) creatures. And as for those who believe and do good works, We shall remit from them their evil deeds and shall repay them the best that they did. We have enjoined on man kindness to parents; but if they strive to make thee join with Me that of which thou hast no knowledge, then obey them not. Unto Me is your return and I shall tell you what ye used to do. And as for those who believe and do good works, We verily shall make them enter in among the righteous.
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21. Arabic Morphological Analysers and Stemmers
School of Computing
FACULTY OF ENGINEERING
Evaluating stemming and morphological analyzers.
A comparison of three stemming algorithms has been done.
Shereen Khoja Stemmer, Tim Buckwalter morphological analyzer and tri-literal root extraction algorithm.
Four different fair evaluation measurements were applied.
A combining by voting is used to combine results of different algorithms.
The paper shows that more work in this field is required as the stemming algorithms failed to achieve accuracy rates more that 75% (sawalha & Atwell, 2008).

22. Prior-Knowledge broad-lexical resource of Arabic Language
School of Computing
FACULTY OF ENGINEERING
15 Arabic language dictionaries* are used
The lexicon contains:
roots and single words.
Multi-word expressions.
Idioms.
Collocations requiring special part of speech assignment.
Words with special part of speech tags.
Meanings.
I've seen it all..;)
* Freely available from in MS-Word format

23. Prior-Knowledge broad-lexical resource of Arabic Language
School of Computing
FACULTY OF ENGINEERING
Lisan Al-Arab “ لسان العرب ” Arab tongue
Taj Al-Arous min jawaher Al-Qamus “تاج العروس من جواهر القاموس ” Bride crown from the dictionaries jewels

24. Existing Arabic language Part-of-Speech taggers and reuse
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FACULTY OF ENGINEERING
Evaluating existing Part-of-Speech tagger components.
Gold Standard
Fair measurements
Multi-level tagset
Analyzing & re-implementing algorithms of Part-of-Speech taggers.
Best tagger components need to be re-implemented, using Python.
Python will simplify the integration of the Part-of-Speech tagger to the NLTK (Natural Language Toolkit).

25. Hybrid Part-of-Speech tagger
School of Computing
FACULTY OF ENGINEERING
Novel algorithm leading to hybrid Part-of-Speech tagger for Arabic text which combines best components of existing taggers with novel resources and components.
Integrating best tagger components together
Integrating Prior-knowledge lexical resource
Integrating Morphological analyser
Using unsupervised learning algorithms to solve the problem of unknown words.

26. School of Computing
FACULTY OF ENGINEERING
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