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	
MII1008	Computational Approaches to Natural Language Semantics	

Semester	Hours: C+S+L	Category	Status	
4	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

      The goal of this course is to present the main aspects of Computational Approaches to Natural Language Semantics. Handling human language by computer arises some important semantic questions as for example: semantic similarity  within a set of documents or terms,based on the likeness of their meaning/semantic content.  Also, in the semantic web era, a growing number of  semantic applications started to access and interoperate through the internet. A central concept is  
      that of  domain ontology,  which  represents the particular meanings of terms as they apply to that domain, and  text mining, which  includes  text categorization,  text clustering, concept/entity extraction, sentiment analysis, document summarization, and entity relation modeling. All these aspects are presented in this course.

IV. Course contents
1. Text entailment. 
2. A lexical-semantic dictionary, WordNet.
3. Discourse structure: Reference (anaphora) resolution, Dialogue, Automatic interpretation of dialog acts.
4. Discourse segmentation . Discourse coherence. 
5. Text Mining:  Text summarization, Text categorization, Information Retrieval 
6. Intensional logic of Montague. Translation of expressions from natural language to intensional logic. Glyn Morrill and Lambek calculus. Compositional semantics.	

V. Bibliography
1. J.ALLEN: "Natural language understanding", Benjamin/Cummings Publ., 2nd ed., 1995. 
2. D.JURAFSKY, J.MARTIN: "Speech and language processing", Prentice Hall, 2000.
3. A.LECOMTE: "Grammaire et theorie de la preuve. Une introduction", T. A. L., 1996, vol 37, no.2, pp 3-37. 
4. C.MANNING, H.SCHUTZE: "Foundation of statistical natural language processing", MIT, 1999.
5. R. MITKOV: "Anaphora resolution", Ed. Longman, 2002. 
6. G.MORRILL: "Type LogicalGrammar.Categorial Logic of Signs", Kluwer Academic Publishers, 1994. 
7. S.J.RUSSELL, P.NORVIG: "Artificial intelligence.A modern approach", Prentice-Hall 
8. D.TATAR: "Inteligenta artificiala: demonstrare automata de teoreme, prelucrarea limbajului natural", Editura Albastra, Microinformatica, 2001.
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. Text entailment. Systems participating at RTE-1 and RTE-2 Challenges of recognising text entailment.  (Ref  1,2,4,8,9 ) 3 lectures 
2. A lexical-semantic dictionary, WordNet. Semantic relations: synsets,  hypernyms, hyponyms, meronoyms, antonyms. (Ref  4,10,11,12 ) 2 lectures 
3. Discourse structure. Reference (anaphora) resolution : Mitkov Algorithm and KB algorithm. (Ref 5) 1 lecture 
4.  Dialogue : Feature Structures in dialog representation, Automatic interpretation of dialog acts (Ref  2,4,9,10) 
1 lecture 
5.  Discourse segmentation. Discourse coherence. Conversational agents. (Ref  2,4,9,10) 2 lecture 
6. Text Mining:  
   -Text summarization (Ref  4,10,12) 1 lecture 
   -Text categorization  (Ref  4,10,12 ) 1 lecture 
   -Information retrieval (Ref  4,10,12 ) 1 lecture 
Retrieval. 
7. Intensional logic of Montague. Translation of expressions from natural language to intensional logic. Glyn Morrill and Lambek calculus. Compositional semantics. Categorial grammars  (Ref 3,6) 2 lectures

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 supervision  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 2\  or related to the topics of course  from  http://acl.ldc.upenn.edu/ … 2 points.

(2)	An application  using  Computational Approaches to Natural Language Semantics 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.  Papers on NLP in ResearchIndex, http://citeseer.csail.mit.edu/

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

13. 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.