Arabic Natural Language Processing: State of the Art and Prospects

1. Arabic Natural Language Processing: State of the Art and Prospects Rached Zantout, Ph.D. Electrical and Computer Engineering Department Hariri Canadian University Mechref, Chouf, Lebanon

2. Outline • What is NLP ? • Why NLP? • MT as a case study! • Problems solved by MT. • Main players in MT. • How does Arabic compare to other Languages as far as NLP is concerned? • MT as a case study. • What kind of research is being conducted in ANLP? • Recommendations! Zantout ANLP: State of the Art and Prospects

3. 1. Coming together of Symbolic and Statistical traditions; 2. Increased focus on functionality and less on representation; NL and Speech Applications 3. Availability of large corpora, large disk space, commoditization of computing resources. 4. Emergence of the World Wide Web, continues to change the field…. Mid-90s- present 1. Revival of Finite-state Models for NL processing; XEROX, AT&T 2. Computational implementation of large NL Grammars in different grammatical frameworks and 3. Development of Penn Treebank; a parse annotated corpora 4. Machine learning coming of age; Mid-80s; mid-90s • Symbolic tradition: (CS) • a. How much representational power is needed for NL? Grammars of increasing power to describe NL: Joshi, Gazdar, Bresnan, Kaplan, Periera, Warren, Shieber. • b. AI Researchers: Winograd, Schank, Wilks, Lenhart, Woods: Understanding systems; SHRDLU, scripts, plans and goals LUNAR • c. Discourse and Dialog Structure: Grosz, Sidner, Hobbs, Perrault, Cohen • 2. Statistical tradition: (EE.) Hidden Markov models for speech recognition; 1970s; mid-1980s 1. Symbolic tradition: (CS) Generative grammar; parsing algorithms; Newell, Simon, Shannon, McCarthy, Minsky, Rochester: Birth of AI; pattern matching based NL understanding system. 2. Statistical tradition: (EE.) Probabilistic inferences for OCR; representationally-light models 1960s 1. Kleene’s and Shannon’s probabilistic finite automaton; Chomsky’s context-free grammars; programming languages; formal language theory 2. Shannon’s information theory – information can be measured; decoding paradigm 1950s 1939-1945 World War II; Need for code-breaking algorithms; ENIAC 1936 Turing’s model of computation; theoretical basis for computer science Tracing the history of NLP Zantout ANLP: State of the Art and Prospects

4. NL and NLP definitions adapted from http://www.cs.bham.ac.uk/~pxc/nlpa/index02.htm • 'natural language' (NL): • Any of the languages naturally used by humans, • not an artificial or man-made language such as a programming language. • (Arabic, English, Chinese, Swahili, etc.) • evolved over thousands of years. • efficient vehicles for human to human communication. • 'Natural language processing' (NLP): • attempts to use computers to process a NL. • Enter computers. • What's the connection? Zantout ANLP: State of the Art and Prospects

5. Why ? adapted from http://www.cs.utexas.edu/users/ear/cs378NLP/ • Is there any reason a computer should know English or Chinese or Swahili? • Yes. There are several "killer apps" for NLP: • retrieving information from the web, • translating documents from one language to another, and • spoken front ends to all kinds of application programs. Zantout ANLP: State of the Art and Prospects

6. NLP includes adapted from http://www.cs.bham.ac.uk/~pxc/nlpa/index02.htm • Speech synthesis: • is this very 'intelligent‘? • synthesis of natural-sounding speech is technically complex: • requires some 'understanding' of what is being spoken to ensure, for example, correct intonation. (bear vs. dear) • Speech recognition: • reduction of continuous sound waves to discrete words. • Natural language understanding: • moving from isolated words (written or via speech recognition) • to 'meaning'. • Natural language generation: • generating appropriate NL responses to unpredictable inputs. • Machine translation (MT): translating one NL into another Zantout ANLP: State of the Art and Prospects

7. Areas Related to NLP • Input: • Speech Recognition. • Natural Language Understanding. • Lip Reading ? • Processing: • Information Retrieval: • Finding where textual resources reside. • Information Extraction: • Extracting pertinent facts from textual resources. • Inference: Drawing conclusions based on known facts. • Spelling Correction. • Grammar Checking. • Output: • Natural Language Generation. • Speech Synthesis. • Machine Translation. • Conversational Agents. Zantout ANLP: State of the Art and Prospects

8. NLP taken from http://tangra.si.umich.edu/~radev/NLP/notes/1.ppt • Information extraction • Named entity recognition • Trend analysis • Subjectivity analysis • Text classification • Anaphora resolution, alias resolution • Cross-document cross-reference • Parsing • Semantic analysis • Word sense disambiguation • Word clustering • Question answering • Summarization • Document retrieval (filtering, routing) • Structured text (relational tables) • Paraphrasing and paraphrasing/entailment ID • Text generation • Machine translation Zantout ANLP: State of the Art and Prospects

9. Noun phrase parser Paraphrase identification Question answering NL access to databases Named entity tagging Rhetorical parsing Anaphora resolution, entity crossreference Document and sentence alignment Using bioinformatics methods Encyclopedia Information extraction Speech processing Sentence normalization Text summarization Sentence compression Definition extraction Crossword puzzle generation Prepositional phrase attachment Machine translation Generation Semi-structured document parsing Semantic analysis of short queries User-friendly summarization Number classification Domain-specific PP attachment Time-dependent fact extraction Sample projects Zantout ANLP: State of the Art and Prospects

10. Main research forums and other pointers • Conferences: ACL/NAACL, SIGIR, AAAI/IJCAI, ANLP, Coling, HLT, EACL/NAACL, AMTA/MT Summit, ICSLP/Eurospeech • Journals: Computational Linguistics, Natural Language Engineering, Information Retrieval, Information Processing and Management, ACM Transactions on Information Systems, ACM TALIP, ACM TSLP • University centers: Columbia, CMU, JHU, Brown, UMass, MIT, UPenn, USC/ISI, NMSU, Michigan, Maryland, Edinburgh, Cambridge, Saarland, Sheffield, and many others • Industrial research sites: IBM, SRI, BBN, MITRE, MSR, (AT&T, Bell Labs, PARC) • Startups: Language Weaver, Ask.com, LCC • The Anthology: http://www.aclweb.org/anthology Zantout ANLP: State of the Art and Prospects

11. NLP Sources • Journals: • Artificial Intelligence. • Computational Intelligence. • IEEE Transactions on Intelligent Systems. • Journal of Artificial Intelligence Research. • Cognitive Science. • Machine Translation. • Conferences: • AAAI: American Association for Artificial Intelligence. • IJCAI: International Joint Conference on Artificial Intelligence. • Cognitive Science Society Conferences. • DARPA Speech and Natural Language Processing Workshop. • ARPA Workshop on Human Language Technology. • Machine Translation Summit series of conferences. • TALN series of conferences. • COLING series of conferences. • Collection of papers: • Readings in Natural Language Processing. Zantout ANLP: State of the Art and Prospects

12. Why NLP? Numbers Information age! Information revolution! • Cheaper PCs • Advances in networking • Internet/www central pillar of modern societies • Massive production of information • Growth of www? • 800 Million Documents as of Sep. 1999 • People? US: 6.5 M new adult users between 2/99 & 5/99 World: 26 Million in 1995 163.25 Million as of 9/99 Zantout ANLP: State of the Art and Prospects

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14. More Recent Statistics (2006) Zantout ANLP: State of the Art and Prospects

15. Web Characterization: Country Statisticshttp://www.oclc.org/research/projects/archive/wcp/stats/intnl.htm Zantout ANLP: State of the Art and Prospects

16. Web Characterization: Language Statisticshttp://www.oclc.org/research/projects/archive/wcp/stats/intnl.htm Zantout ANLP: State of the Art and Prospects

17. What’s the Use of the Numbers? • Prove that there is a “Linguistic Problem”: • Domination of the English Language. • Alienates non-English Speakers. • Computers are our interface to the internet: • Computers do not understand a Natural Language. • We do not have enough time to guide computers to do what is required of them • E.g. Search for all presentations about NLP on the internet. • Digest them and produce one presentation appropriate for my talk at UOB ;-) Zantout ANLP: State of the Art and Prospects

18. What’s the Use of the Numbers? • Middle-East is a growing internet market: • Growing very fast. • Lots of Arabs (read non-English speakers). • Need to communicate with my own language. • Need computer to save time for me while searching for information. • Dream: computer could do most of my work and I can just relax  • Introducing the A into ANLP. Zantout ANLP: State of the Art and Prospects

19. The Linguistic ProblemMachine Translation (MT) a Case Study English: the de-facto international language • Internet and www (“CyberEnglish”!) • Science and Technology • Trade and Industry • Politics and Media • Tourism • Etc. • English = key to accessing Knowledge • in all walks of life! • Alienation of the HUGE majority of world population • Impoverishment of world cultures Zantout ANLP: State of the Art and Prospects

20. The Linguistic Challenge France: • 1997: 7% French presence on www • Legislation introduced (forcing I. Content providers to translate web sites into French) • Pres. Chirac: “If in the new media, our language, our programs, our creations, are not strongly present, the young generation of our country will be economically and culturally marginalized” • “I do not want to see the European Culture sterilized or obliterated by the American Culture” French is stronger than Arabic on the internet and the PC. Zantout ANLP: State of the Art and Prospects

21. If not General NLP! How about at least MT? • Languages in the world • 6,800 living languages • 600 with written tradition • 95% of world population speaks 100 languages • Translation Market • $8 Billion Global Market • Doubling every five years • (Donald Barabé, invited talk, MT Summit 2003) Zantout ANLP: State of the Art and Prospects

22. The Problem • Coping with the huge amount of articles, books, patents in all disciplines (Assimilation) • Coping with the www massive volume • Exporting economic products (Dissemination) • Facing the Omnipresence of English 50% of all scientific and technical references Linguistic, cultural, social, educational, economic, and political factors Zantout ANLP: State of the Art and Prospects

23. Human Translation too limited  MT Translation Cost in EU is $1 Billion Official Languages: from 11 to 20 1600 Human Translators Zantout ANLP: State of the Art and Prospects

24. Why Machine Translation? • Full Translation • Domain specific • Weather reports • Machine-aided Translation • Translation dictionaries • Translation memories • Requires post-editing • Cross-lingual NLP applications • Cross-language IR • Cross-language Summarization Zantout ANLP: State of the Art and Prospects

25. MT: A Strategic Choice • USA: FCCSET report on MT (1993) on the president’s request. • Japan: $200 Million during 15 years till 1991. (Asian Multilingual MTS since 87) • EU: since 1991, 220 projects on Language Technology ($30 million on Eurotra!) 1996 report on the state of MT Zantout ANLP: State of the Art and Prospects

26. MT Players • Governments: US, European, Japan, Canada, ex-USSR (cold war), Korea, Malaysia, Indonesia, Thailand, etc. • International institutions: • UN, E. Commission (12 languages; soon to be 22/23!!), etc. • Companies (R&D):Microsoft, Siemens, Fujitsu, Hitachi, Toshiba, Oki, NEC, Mitsubishi, Sharp… Zantout ANLP: State of the Art and Prospects

27. MT Market • World: estimated at $20 billion in 1991 • MT Tools Market: $20 million in 1994 • > 160 language pairs • > 60 MTSs being developed (as of 98) • Globalink claims 600 K users of its MTS • Lang. Eng. Corp. income (LogoVista): $2M • Smart Communications (Smart Translator): $6M • Systran (12 languages): 60,000 pages/year Zantout ANLP: State of the Art and Prospects

28. AMT Zantout ANLP: State of the Art and Prospects

29. ANLPAsharqAlawsat (الشرق الأوسط) 09.10.03 Zantout ANLP: State of the Art and Prospects

30. ANLP State Compared to General NLP • Script problem: • Arabic characters are nowhere near Latin-Based Characters. • Lack of funding: • Governments. • Pan-Arab Organizations. • Industry ?! Private Sector. • Research ??? • Infrastructure ! Zantout ANLP: State of the Art and Prospects

31. Progress in Western MTStatistical MT example Form a talk by Charles Wayne, DARPA Zantout ANLP: State of the Art and Prospects

32. A First taste of Arabic Machine Translation • English Text: • Before more than 30,000 fans who headed to the Cite Sportive from all Lebanese region on Sunday Nejmeh drew 1-1 with their traditional rivals Ansar in a breathtaking showdown, which saw both teams performing their best. • Human Translation: • أمام أكثر من 30.000 متفرج زحفوا إلى ملعب المدينة الرياضية نهار الأحد تعادل النجمة و الانصار 1-1 في مباراة مثيرة قدّم خلالها الفريقان عرضاً طيّباً افتقدته الملاعب اللبنانية منذ فترة طويلة. • Ajeeb Translation: • قبل أكثر من 30،000 معجب الّذين اتّجهوا إلى اليذكر لعوب من كلّ المنطقة اللّبنانيّة يوم الأحد نجمة رسم 1-1 مع ترادي Zantout ANLP: State of the Art and Prospects

33. A 1st Taste of Arabic MT • A sample of sentences to be translated: • Quite disappointing! • But, need for a more formal assessment and closer scrutiny Zantout ANLP: State of the Art and Prospects

34. Multilingual Challenges Morphological Variations • Affixation vs. Root+Pattern Zantout ANLP: State of the Art and Prospects

35. Translation Divergences conflation ليس be etre ا نا هنا I not here Je ne pas ici لست هنا I-am-not here I am not here Je nesuispas ici I notbenot here Zantout ANLP: State of the Art and Prospects

36. Translation Divergencescategorial, thematic and structural * be ا نا بردان I cold انا بردان I cold I am cold Zantout ANLP: State of the Art and Prospects

37. اسرع انا عبور سباحة swim نهر I Swam across quickly river Translation Divergenceshead swap and categorial I swam across the river quickly اسرعت عبور النهر سباحة I-sped crossing the-river swimming Zantout ANLP: State of the Art and Prospects

38. اسرع انا عبور سباحة swim نهر I across quickly river Translation Divergences head swap and categorial verb noun prep noun adverb verb Zantout ANLP: State of the Art and Prospects

39. Fluency vs. Accuracy FAHQ MT conMT Prof. MT Fluency Info. MT Accuracy Zantout ANLP: State of the Art and Prospects

40. Evaluation of MTSs • Various methodologies put forward • Various aspects considered: Intelligibility, Fidelity, and other software engineering features • Mostly human-centered: • Get users to compare Human and M. T. • Get users to evaluate MT output on a scale (e.g. 1-5) • Subjective to a large extent Zantout ANLP: State of the Art and Prospects

41. Automatic Evaluation ExampleBleu Metric Test Sentence colorless green ideas sleep furiously Gold Standard References all dull jade ideas sleep irately drab emerald concepts sleep furiously colorless immature thoughts nap angrily Zantout ANLP: State of the Art and Prospects

42. Automatic Evaluation ExampleBleu Metric Test Sentence colorless green ideassleepfuriously Gold Standard References all dull jade ideassleep irately drab emerald concepts sleepfuriously colorless immature thoughts nap angrily Unigram precision = 4/5 Zantout ANLP: State of the Art and Prospects

43. Automatic Evaluation ExampleBleu Metric Test Sentence colorless green ideas sleep furiously colorless green ideas sleep furiously colorless greenideas sleepfuriously colorless green ideassleep furiously Gold Standard References all dull jade ideassleep irately drab emerald concepts sleepfuriously colorless immature thoughts nap angrily Unigram precision = 4 / 5 = 0.8 Bigram precision = 2 / 4 = 0.5 Bleu Score = (a1 a2 …an)1/n = (0.8╳ 0.5)½ = 0.6325  63.25 Zantout ANLP: State of the Art and Prospects

44. Evaluating AMT’s • 3 Arabic MT systems tested: - Al-Mutarjim Al-Arabey (ATA Software Tech.) - Al-Wafi (by ATA Software Tech.) - Arabtrans (by Arab.Net Tech.) • Sample texts translated. • Scoring by a human (1 or 0.5 or 0 ) • Results: Zantout ANLP: State of the Art and Prospects

45. Analysis of the results • Poor AMT systems overall • Good Lexicon coverage in the domain “Internet and Arabisation” • Very Poor Grammatical results: • detailed analysis focuses on bad areas. • Pronoun resolution and semantic correctness • barely above average • (almost 1 error out of each 2 cases!) • The technology used in AMTS’s is “outdated” Zantout ANLP: State of the Art and Prospects

46. Future Work • Develop awareness of the importance of MT and NLP for Arabic. • Developing our own MT system based on all what we have learned from the evaluation • Focus on Statistical techniques: • Speed of Implementation. • Obtaining better results. Zantout ANLP: State of the Art and Prospects

47. AMT and LebanonECOMLEB, no.2, 1st Quarter 2005 • “How can you explain why so many in the IT Field can’t find a job in Lebanon when we keep hearing that we are the best in the region?”, Reader’s Comments, P. 02. • “Khan Al-Saboun”, a local soap maker in Tripoli now sells soaps all over the world. “University Series, p. 05” • “… Lebanon has one of the highest rates of internet usage in the area, a good PC penetration, abundant human talent and resources in IT and particularly software and web design, and no money transfer restrictions” Interview with Minister of Economy and Trade, H.E. Adnan Kassar, p. 16. • “…[Lebanon needs to] reduce brain drain” Interview with Minister of Economy and Trade, H.E. Adnan Kassar, p. 17. • “…[Lebanon has] a multiligual and highly educated human resource [base]” Interview with Minister of Economy and Trade, H.E. Adnan Kassar, p. 17. • “B2C e-commerce is expected to cross US$ 1 Billion mark by 2008 in GCC countries … particularly in e-shopping … mainly in Saudi Arabia and the UAE … compund average growth of 22% over 5 years … > 33.33% of transactions are booking for airline and hotels. Zantout ANLP: State of the Art and Prospects

48. Recommendations • Develop Arab acceptance of the strategic nature of ANLP/AMT • Establishing an Arab Centre for Arabic language processing and AMT • Gather Arab researchers • Host and sponsor research: • Morphology, • Parsing, • Speech • semantics, pragmatics • Building a central repository: • software, • lexicons, • corpora, • Tools • and archive (literature) Zantout ANLP: State of the Art and Prospects

49. Recommendations (cont.) • Strengthen ties between Academia, research centers, and industry • Sponsor Pan-Arab projects (ESPRIT-like) • Sponsor conferences, exhibitions, and trade shows: • Coordinate Different Conferences: • 2 upcoming ANLP conferences AT THE SAME TIME in 2 Different places (KSA and Morocco) • Plan for a third (UAE). • Strengthen links with western institutions (on NLP/MT): • Already western researchers are active in ANLP: • A workshop in London in the same time frame as both conferences in KSA and Morocco. Zantout ANLP: State of the Art and Prospects

50. Thank you for your patience! • References: • Ahmed Guessoum, Rached Zantout, A Methodology for Evaluating Arabic Machine Translation Systems, Machine Translation, Volume 18, Issue 4, Dec 2004, Pages 299 - 335 • R. Zantout and A. Guessoum, An Automatic English-Arabic HTML Page Translation System, Journal of Network and Computer Applications, vol. 4, no. 24, October 2001. • Guessoum and R. Zantout, A Methodology for a semi-automatic evaluation of the language coverage of machine translation system lexicons, The Journal of Machine Translation, Kluwer Academic Publishers, The Netherlands, vol. 16, October 2001. • Zantout, Rached and Guessoum, Ahmed, Arabic Machine Translation: A Strategic Choice for the Arab World, Journal of King Saud University, Vol. 12, Computer and Information Sciences, pp. 117-144, A.H. 1420-2000. • Ahmed Guessoum, Rached Zantout, Machine Translation, A Startegic Dimension for the Arab World, University Forum, University of Sharjah, Issue 41, Year 6, Muharram 1427, February 2006, pp. 32-37. • Guessoum, Ahmed and Zantout, Rached, Arabizing the Internet and its effect on the development of the Kingdom of Saudi Arabia, The 100 years symposium of the King Saud University, Riyadh, Saudi Arabia, 18-19/10/1999. • Guessoum, Ahmed and Zantout, Rached, Towards a Strategic Effort, with a Central Theme of Machine Translation, to meet the challenges of the Information Revolution, 1998 Symposium of Proliferation of Arabization and Development of Translation in the Kingdom of Saudi Arabia, King Saud University, Riyadh. • “Machine Translation: Challenges and Approaches,” Invited Lecture, CS 4705: Introduction to Natural Language Processing Fall 2004, Nizar HabashPost-doctoral Fellow, Center for Computational Learning Systems, Columbia University. Zantout ANLP: State of the Art and Prospects