The Floating Arabic Dictionary: AnAutomatic Method for Updating a Lexical Database through the Detection and Lemmatization of Unknown Words Mohammed Attia, Younes Samih, Khaled Shaalan and Josef van Genabith Faculty of Engineering and IT, The British University in Dubai Heinrich-Heine-Universität, Germany School of Computing, Dublin City University, Ireland
Outline• Introduction• Morphological Guesser• Methodology• Testing and Evaluation• Conclusion
Introduction• Why deal with unknown words?• Complexity of lemmatization in Arabic• Data used
IntroductionA living language is just… living… dynamic… constantly changing… new words appear… old words die out… some words are seasonal… some are core
IntroductionWhy deal with unknown words?• Language is always changing • New words appear • Old words disappear • Unknown words make up 29% of the Gigaword corpus• Unknown words (OOV) always cause a problem to: • Morphological analysers • Parsers • Machine Translation & other applications
Review of Arabic lexicographic workKitab al-Ain by al-Khalil bin Ahmed al-Farahidi (died 789) (refinement/expansion/organizational Improvement) ▼• Tahzib al-Lughah by Abu Mansour al-Azhari (died 980)• al-Muheet by al-Sahib bin Abbad (died 995)• Lisan al-Arab by ibn Manzour (died 1311)• al-Qamous al-Muheet by al-Fairouzabadi (died 1414)• Taj al-Arous by Muhammad Murtada al-Zabidi (died 1791)• Muheet al-Muheet (1869) by Butrus al-Bustani• al-Mujam al-Waseet (1960)• Buckwalter Arabic Morphological Analyzer (2002) Size: 40,222 lemmas (including 2,034 proper nouns) Includes many obsolete lexical items Many modern words are missed out
Review of Arabic lexicographic work Not in Dictionaries: about 10,000 need to be addedسياسة: أمننة شرعنة أفروعربية إثني إقصائي تسييس محاصصة جبهوي جمهوعسكرية العصبوية شخصنة أمركة عصرنة تكنولوجيا: َْ رقمنة، أتمتة، مك َننة فيس بوك، تويتر، تغريدة هاتف جوال تليفون محمول الب توب الهواتف الذكية حوسبة بريد إلكتروني دي في دي، سي دي سبام، فيروس ملتي ميديا كمبيوتر لوحي، شاشة لمسية شيفرةاقتصاد: خصخصة ريعي يورو بورصة تعويم داو_جونز تضخم أسهم قيمة_دفترية مليار ترليون تجارة_إلكترونية
Review of Arabic lexicographic work Not in Dictionaries: about 10,000 need to be addedPolitics: legalizing, Afro-Arab, ethnic, ostracizing, Americanize, modernize Technology: Digitalizing, automating, Mechanizing Facebook, twitter, tweet Mobile phone Laptop Smartphone Computerizing Email DVD, CD Span, virus Multimedia Tablet PC, touch screenEconomy: privatization, Euro, inflation, Billion, Trillion, e-commerce
IntroductionComplexity of lemmatization in Arabic• Lemmatization means reducing words to their base (canonical) forms • played -> play studies - study • went -> go wives -> wife• New words in English appear in their base form 86% of the time (Lindén, 2008)• New words in Arabic appear in their base form 45% of the time• Arabic morphology is complex and semi-algorithmic: root, patterns, inflections, clitics, etc.
Introduction وسيشكرونه wasayashkurunahu wa@sa@yashkuruna@huComplexity of lemmatization in Arabic and@will@thank[they]@him Proclitics Prefix Lemma Suffix EncliticConjunction/ Comp Tense/mood – Verb Tense/mood – Objectquestion article number/gend number/gend pronounConjunctions ل وli ‘to’ Imperfective Imperfective First personwa ‘and’ or فfa tense (5) tense (10) (2)‘then’Question word س أsa ‘will’ Perfective tense lemma Perfective lemma Second᾽a ‘is it true that’ (1) tense (12) person (5) لla ‘then’ Imperative (2) Imperative (5) Third person (5)Possible Concatenations in Arabic Verbs شكرšakara ‘to thank’, generate 2,552 valid forms
Introduction وللمدرسين walilmudarrisiyna wa@li@al@mudarrisiynaComplexity of lemmatization in Arabic and@to@the@teachers Proclitics lemma Suffix Enclitic Conjunction/ Preposition Definite Noun Gender/Number Genitive question article article pronoun Conjunctions ب وbi ‘with’, الal ‘the’ Masculine Dual First person wa ‘and’ or ف كka ‘as’ (4) (2) fa ‘then’ or لli ‘to’ Feminine Dual (4) Question word أ Stem lemma Masculine Second person ᾽a ‘is it true regular plural (5) that’ (4) Feminine Third person regular plural (5) (1) Feminine Mark (1) مدرسmudarris ‘teacher’, generate 519Possible Concatenations in Arabic Nouns valid forms
IntroductionDifference between stemming and lemmatizing وسيقولونها wa-sa-ya+quwl+uwna-ha and they will say it Stemming Lemmatizing quwl qAla قول قال Alteration rules
IntroductionData used• A large-scale corpus of 1,089,111,204 words • 85% from the Arabic Gigaword Fourth Edition • 15% from news articles crawled from the Al- Jazeera web siteIf printed on paper, it will be more than 2 times the height of EiffelTower= 16,000 large books= 640 meters of bookshelvesAvr reader reads 200 wpm with 60% comprehension.You will need 11 years 24/7 to read the Gigaword corpusTechnical issues:20-30 days to analyze with MADA using 10 parrallel sessions.You will need a machine with 256GB RAM to read 3-,4-. Or 5-gram language model of the Arabic Gigaword
Morphological GuesserWe develop a morphological guesser forArabic unknown words that handles allpossible • Clitics • Prefixes • Suffixes • And all relevant alteration operations that include insertion, assimilation, and deletion
MethodologyWe use a pipeline-based approach• First: a machine learning (SVM), context-sensitive tool (MADA) is used to predict: • POS • Morpho-syntactic features of number, gender, person, tense, etc.• Second: The finite-state morphological guesser is used to produce all the possible interpretations of words and suggested lemmas.• Third: The two output are matched together and the agreed analysis is selected.
MethodologyResults• Corpus size is 1,089,111,204 tokens, 7,348,173 types• Unknown Types in the corpus: 2,116,180 (29%)• After spell checking, correctly spelt types are 208,188• Types with frequency of 10 or more: 40,277• After lemmatization:18,399 types
Testing and Evaluation Gold POS Type Count RatioWe create a gold standard noun_prop 584 45 % noun 264 20 %of 1,310 words manually- adj 255 19 %annotated for: verb 52 4% noun_fem_plural 28 2%• Gold lemma (pluralia tantum)• Gold POS noun_broken_plural 28 2% others: 8 0.6 %• Lexical relevance (include in a noun_masc_plural (pluralia tantum) (4) part dictionary): yes or no (3) pron_dem (1)Among unknown words, Excluded misspelling 55 4%- Proper nouns are the most common not_known 15 1%- Verbs are the least common colloquial 19 1.5 % Lexicographic relevance Include in a dictionary 671 51 % Don’t include in a 639 49 % dictionary
Testing and EvaluationEvaluating POS (accuracy)• Baseline: The most frequent tag (proper name) for all unknown words: 45%• Mada: 60%• Voted POS Tagging: 69%. When a lemma gets a different POS tag with a higher frequency we take the higher Accuracy POS tagging 1 POS Tagging baseline 45% 2 MADA POS tagging 60% 3 Voted POS Tagging 69%
Testing and EvaluationEvaluating Lemmatization (accuracy)• Baseline: new words appear in their base form: 45%• Pipelined strict definite article ‘al’: 54%• Pipelined ignoring definite article ‘al’: 63% Lemmatization 1 Lemma first-order baseline 45% 2 Pipelined lemmatization (first- 54% order decision) with strict definite article matching 3 Pipelined lemmatization (first- 63% order decision) ignoring definite article matching
Testing and EvaluationEvaluating Lemma Weighting• The weighting criteria aims to push lexicographically relevant words up the list and less interesting words down.• We aim to make the number of important words high in the top 100 and low in the bottom 100Word Weight = ((number ofsister forms * 800) + Good words In top In bottomfrequencies of sister forms) / 2 + 100 100POS factor relying on Frequency 63 50 alone (baseline) relying on number of 87 28 sister forms * 800 relying on POS factor 58 30 using combined criteria 78 15
Testing and EvaluationOxford new words list: June 2012• BitTorrent: a protocol that underpins the practice of peer-to-peer file sharing• command line: a user interface that is navigated by typing commands• cybercast: A news or entertainment program transmitted over the Internet.• subcommunity: a distinct grouping within a community• subjectivization: to make subjective• subpersonality: a personality mode that kicks in (appears on a temporary basis) to allow a person to cope with certain types of psychosocial situations.• superglue v: to stick with superglue
Testing and EvaluationWords expected in the next Arabic dictionary/morphological analyser
Bird’s Eye viewProblem • Out of Vocabulary words (OOV) cause a problem to morphological analysers, parsers, MT, etc. • The manual extension of lexical databases is costly an time consuming. • With the large amount of data, manual extension of lexicons becomes practically impossible.Solution • Creating an automatic method for updating a lexical database • Integrating a Machine Learning method with a finite state guesser to lemmatize unknown words • Weighting new words by relevance and importance
Conclusion• We develop a methodology for automatically extracting and lemmatizing unknown words in Arabic• We pipeline a finite-state guesser with a machine learning tool for lemmatization• We develop a weighting mechanism for predicting the relevance and importance of lemmas• Out of 2,116,180 unknown words, we create a lexicon of 18,399 lemmatized, POS-tagged and weighted entries.
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