Universitatea Babeş-Bolyai Cluj-Napoca
Facultatea de Matematică şi Informatică
Ciclul de studii: Masterat
Domeniul: Informatica
Programul de studii: Sisteme Inteligente - în limba engleză
Limba de predare: Engleză

SYLLABUS

I. General data
Code	Subject	
MII1005	Language Technology	

Semester	Hours: C+S+L	Category	Status	
2	2+2+0	speciality	compulsory	

II. Full status faculty members
Name and	Scientific	Didactic	Chair	Type of activity	
surname	title	title		C	S	L	
TATAR Doina	Ph.D.	Prof.	Programming  Languages and Methods	*	*		

Associated faculty members
Name and	Scientific	Institution	Type of	Type of activity	
surname	title		position	C	S	L	

III. Course objectives
•	Language technology (LT) is a subfield of  artificial intelligence. It studies the problems of automated generation and understanding of natural human languages.  The course has as objectives to provide the basic principles, technologies and applications of  LT . 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 LT.  A deep  understanding of the current state of the art in LT  in order to realize  original research in LT is an other  goal of this course.

IV. Course contents
1. Introduction in Natural Language Processing and Language Technology: Stages, Domains, Chapters. 
 2. The corpus as a tool of different applications in  LT
 3. Word Sense Disambiguation: machine learning approach (supervised and unsupervised), dictionary based approach.
 4.  Statistical natural language processing  and LT: Markov chains, Hidden Markov Model. Evaluation, Estimation and Training with HMM.  Applications.
 5. Probabilistic Context- free Grammars.  Syntactic analysis: active charts.  
 6. Unification grammars. Feature structures as graphs, AVM and descriptors. Parsing  with unification grammars.	

V. Bibliography
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.

VI. Thematic of didactic activities per weeks
1. Introduction in Natural Language Processing (NLP): Stages, Domains, Chapters.  (Ref. 1,5, 6, 9,12,13 ) 1 lecture
 2. Corpora. (Ref  4,5,10 ) 1 lecture 
 3. Word Sense Disambiguation.
     - Machine learning approach: supervised (NBC  and k-NN)  (Ref  2,4,9,10,12,13) 1 lecture  

      -  unsupervised (by clustering).  (Ref  2,4,9,10 ,14 )  1 lecture 

      -  Dictionary based approach (Lesk, Yarowsky, bilingual dictionaries) (Ref  2,4,9,10, 14 ) 1 lecture .
 4. Statistics in  NLP: 
       - Markov chains, Hidden Markov Model (Ref  2,5,9,14 ) 1 lecture .
        - Evaluation, Estimation and Training with HMM. The probability of input sequences, the most likely path. (Ref  2,5,9, 11, 14  ) 2 lecture 
        - Applications to POS tagging (Ref  2,5,9, 11, 14  ) 1 lecture  .
 5. Probabilistic Context- free Grammars.  Syntactic analysis: active charts.Earley's algorithm.  
                                                                                (Ref  4,9,10) 1 lecture  
 6. Unification grammars. 

       - Feature structures as objects of linguistic knowledge representation. Feature structures as graphs, AVM and descriptors.  (Ref  3,8,9,14 ) 1 lecture 
       - Subsumation and unification. Proof theory of descriptors. Well-typed and total-well-typed feature structures.  (Ref 3,7, 8,9,14  )  2 lecture 
        - Parsing  with unification grammars (top-down and bottom-up). (Ref   3,8,9,14) 1 lecture 

VII. Didactic methods used
The main methods used are: lectures, presentations of different papers on topics of course,  the stimulation  and supervision of appropriate applications, the stimulation of editing papers for conferences. 

VIII. Assessment
The final note will have the following components :  

(1)	Presentation of a paper  from the list in \\Win\labor\Semestrul 1\  or related to the topics of course  from  http://acl.ldc.upenn.edu/ … 2 points.

(2)	An application  using  LT techniques   ……2points. 

The area of this application will be announced  since a fixed deadline and it will be  finished
since exam. (the announcement after the deadline  will decrease the points with 1)   
                                                                             
       (3) A second paper , as at (1), will be presented until exam…..1point  

       (4) Examination  at exam session ………………………   4points  

  Remark: The final mark is obtained by summing  the points obtained at (1), (2),(3),(4) and 1 (office point). Successful passing of the exam is conditioned by the final mark that has to be at least 5. 

  Remark:  Students who demonstrate excellent research performance by developing  an article accepted at an national/international conference  will  receive the maximal mark.  

IX. Additional references
11. B. Krenn and C. Samuelsson: “The linguist’s guide to statistics”, 1997, at \\Win\labor.

12.  Papers on NLP in ResearchIndex, http://citeseer.csail.mit.edu/

13 . Books and Papers on site of ACL : http://aclweb.org/aclwiki/

 14. Papers on my page:  //www.cs.ubbcluj.ro/~dtatar, and  then, for example,   http://www.cs.ubbcluj.ro/~studia-i/

	Date,							Course responsible,
       April 14, 2007					Prof. TATAR Doina,  Ph.D.