Arabic Natural Language Processing: State of the Art and Prospects Rached Zantout, Ph.D. Electrical...

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

11/2006 UOB Zantout ANLP: State of the Art and Prospects 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!


Tracing the history of NLP

1939-1945

World War II; Need for code-breaking algorithms; ENIAC

1936 Turing’s model of computation; theoretical basis for computer science

1950s

1. Kleene’s and Shannon’s probabilistic finite automaton; Chomsky’s context-free grammars; programming languages; formal language theory2. Shannon’s information theory – information can be measured; decoding paradigm

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. 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, Cohen2. Statistical tradition: (EE.) Hidden Markov models for speech recognition;

1970s; mid-1980s

Mid-80s; mid-90s

1. Revival of Finite-state Models for NL processing; XEROX, AT&T2. Computational implementation of large NL Grammars in different grammatical frameworks and3. Development of Penn Treebank; a parse annotated corpora4. Machine learning coming of age;

Mid-90s- present

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….


NL and NLP definitions• '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?

adapted from http://www.cs.bham.ac.uk/~pxc/nlpa/index02.htm


Why ?

• 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.

adapted from http://www.cs.utexas.edu/users/ear/cs378NLP/


NLP includes• 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

adapted from http://www.cs.bham.ac.uk/~pxc/nlpa/index02.htm


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.


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


Sample projects• 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


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


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.


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 & 5/99World: 26 Million in 1995

163.25 Million as of 9/99

Year 92 93 94 96 Sep. 99

# Sites 50 250 2000 >100K 43 M


More Recent Statistics (2006)


1999 2002

Country Percent of public sites Country Percent of public sites

US 49% US 55%

Germany 5% Germany 6%

UK 5% Japan 5%

Canada 4% UK 3%

Japan 3% Canada 3%

Australia 2% Italy 2%

Brazil 2% France 2%

Italy 2% Netherlands 2%

France 2% Others 18%

Others 16% Unknown 4%

Unknown 10%

Web Characterization: Country Statisticshttp://www.oclc.org/research/projects/archive/wcp/stats/intnl.htm


1999 2002

Language Percent of public sites Language Percent of public sites

English 72% English 72%

German 7% German 7%

French 3% Japanese 6%

Japanese 3% Spanish 3%

Spanish 3% French 3%

Chinese 2% Italian 2%

Italian 2% Dutch 2%

Portuguese 2% Chinese 2%

Dutch 1% Korean 1%

Finnish 1% Portuguese 1%

Russian 1% Russian 1%

Swedish 1% Polish 1%

Web Characterization: Language Statistics http://www.oclc.org/research/projects/archive/wcp/stats/intnl.htm


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 ;-)


• 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.

What’s the Use of the Numbers?


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


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.


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)


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


Human Translation too limited MT

Translation Cost in EU is $1 Billion

Official Languages: from 11 to 20

1600 Human Translators


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


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


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…


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


AMT


ANLPAsharqAlawsat ( األوسط 09.10.03 (الشرق


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 !


Progress in Western MTStatistical MT example

Form a talk by Charles Wayne, DARPA

2002 2003 Human Translationinsistent Wednesday may recurred her trips to Libya tomorrow for flying

Cairo 6-4 ( AFP ) - an official announced today in the Egyptian lines company for flying Tuesday is a company " insistent for flying " may resumed a consideration of a day Wednesday tomorrow her trips to Libya of Security Council decision trace international the imposed ban comment .

Egyptair Has Tomorrow to Resume Its Flights to Libya

Cairo 4-6 (AFP) - said an official at the Egyptian Aviation Company today that the company egyptair may resume as of tomorrow, Wednesday its flights to Libya after the International Security Council resolution to the suspension of the embargo imposed on Libya.

Egypt Air May Resume its Flights to Libya Tomorrow

Cairo, April 6 (AFP) - An Egypt Air official announced, on Tuesday, that Egypt Air will resume its flights to Libya as of tomorrow, Wednesday, after the UN Security Council had announced the suspension of the embargo imposed on Libya.


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:من – أكثر المدينة 30.000أمام ملعب إلى زحفوا متفرج

االنصار و النجمة تعادل األحد نهار في 1-1الرياضيةافتقدته X Zبا طي X عرضا الفريقان خاللها قدZم مثيرة مباراة

. طويلة فترة منذ اللبنانية المالعب

• Ajeeb Translation:من – أكثر 30قبل اليذكر ،000 إلى Zجهوا ات Zذين ال معجب

رسم نجمة األحد يوم Zة Zبناني الل المنطقة Zكل من 1-1لعوبترادي مع


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


Multilingual Challenges Morphological Variations

• Affixation vs. Root+Pattern

write written كتب

بوكتم

kill killed قتل لوقتم

do done فعل لوفعم


هنا تلسI-am-not here

be

I here

I am not here

not

ليس

نا ا هنا

Translation Divergencesconflation

Je ne suis pas iciI not be not here

etre

Je icine pas


*نا ا بردان

بردانانا I cold

be

I cold

I am cold

Translation Divergencescategorial, thematic and structural


swim

I quicklySwamacross

river

I swam across the river quickly

Translation Divergenceshead swap and categorial

اسرع

انا عبورسباحة

نهر

سباحة النهر عبور اسرعتI-sped crossing the-river swimming


Translation Divergences head swap and categorial

اسرع

انا عبورسباحة

نهر

swim

I quicklyacross

rivernoun

prepadverb

verb

verb

noun


Fluency vs. Accuracy

Accuracy

Fluency

conMTFAHQ

MTProf.MT

Info.MT


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


Test Sentence

colorless green ideas sleep furiously

Gold Standard References

all dull jade ideas sleep iratelydrab emerald concepts sleep furiously

colorless immature thoughts nap angrily

Automatic Evaluation ExampleBleu Metric


Test Sentence

colorless green ideas sleep furiously




Unigram precision = 4/5



Test Sentence

colorless green ideas sleep furiouslycolorless green ideas sleep furiouslycolorless green ideas sleep furiouslycolorless green ideas sleep furiously




Unigram precision = 4 / 5 = 0.8Bigram precision = 2 / 4 = 0.5

Bleu Score = (a1 a2 …an)1/n

= (0.8 ╳ 0.5)½ = 0.6325 63.25



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:


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”


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.


AMT and Lebanon ECOMLEB, 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.


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)


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.


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.

Arabic Natural Language Processing: State of the Art and Prospects Rached Zantout, Ph.D. Electrical...

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Transcript of Arabic Natural Language Processing: State of the Art and Prospects Rached Zantout, Ph.D. Electrical...