Subject: Natural Language Processing	Code: MI282	

Semester	 Hours: C+S+L	Type	 Section	
1	2+2+0	compulsory	Intelligent Systems - in English	

Teaching Staff in Charge	Prof. TATAR Doina, Ph.D., dtatar@cs.ubbcluj.ro	
Aims	Natural language processing is now accepted as one of the most studied and active field of Computer Science. The notion of feature structure as linguistic object stands on the base of most recent approaches which are surveyed in this course. The optimization of the search on Web, the interfaces in natural language and the aspects of text mining are only some of  motivations for studying natural language processing	
Content	1. Introduction in Natural Language Processing: Stages, Domains, Chapters. Corpora. 
 2. Word Sense Disambiguation. Machine learning approach: supervised (NBC  and k-NN) and  unsupervised (by clustering). Dictionary based approach (Lesk, Yarowsky, bilingual dictionaries).
 3. Statistics in NLP: Markov chains, Hidden Markov Model. Evaluation, Estimation and Training with HMM. The probability of input sequences, the most likely path. Applications to POS tagging.
 4. Probabilistic Context- free Grammars.  Syntactic analysis: active charts.Earley's algorithm.   
 5. Unification grammars. Feature structures as objects of linguistic knowledge representation. Feature structures as graphs, AVM and descriptors. Subsumation and unification. Proof theory of descriptors. Well-typed and total-well-typed feature structures.  Parsing  with unification grammars (top-down and bottom-up). 

	
Selective References	1. J.ALLEN : Natural language understanding, Benjamin/Cummings Publ. , 2nd ed., 1995. 
2. E. CHARNIAK: "Statistical language learning", MIT press, 1996.  
3. B.CARPENTER: ALE:The attribute logic engine.User's guide. Carnegie Mellon University,1994.
4. D.JURAFSKY, J.MARTIN: Speech and language processing, Prentice Hall, 2000.
5. C.MANNING, H.SCHUTZE: Foundation of statistical natural language processing, MIT, 1999.
6. S.J.RUSSELL, P.NORVIG: Artificial intelligence.A modern approach, Prentice-Hall International,1995.
7. D.TATAR: Inteligenta artificiala: demonstrare automata de teoreme, prelucrarea limbajului natural, Editura Albastra, Microinformatica, 2001.
8. D.TATAR: Unification Grammars in Natural Language Processing, in "Recent topics in mathematical and computational linguistic, ed. C. Martin-Vide, G. Paun, Editura Academiei, 2000, pg 289-300.
9. D. TATAR: Inteligenta artificiala. Aplicatii in prelucrarea limbajului natural,Editura Albastra, Microinformatica, 2003, ISBN 973-650-100-0
10. Editor R. MITKOV: The Oxford Handbook of Computational Linguistics, Oxford University Press, 2003	
Assessment	The examination is by written  exam, with the subjects from all the matter (60%). Will be evaluated the activity of understanding and communication of some recent papers in the field and implementation of some apllications (40%)