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 for  Natural Language Semantics 	

Semester	Hours: C+S+L	Category	Status	
4	2+1+1	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 (TE) as a step of text understanding. Validation of TE by different methods. RTE contests. 
2. Discourse structure. Scoring  methods  for Reference (anaphora) resolution. Lexical chains and anaphora resolution.   Dialogue as a special discourse. 
3. Discourse segmentation. Logical and Lexical methods for discourse segmentation. Discourse coherence and cohesion used in discourse segmentation.
4. Text Mining. Text summarization by extract and abstract. Evaluation of summaries.  Text categorization by clustering.  
5. Natural Language techniques in  Information Retrieval and Information Extraction.  
6. Compositional semantics. Translation of expressions from Natural Language to intensional logic. 

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. H. Helbig: $Knowledge Representation and the Semantics of Natural Language$, Springer, 2006. 
5. D.JURAFSKY, J.MARTIN: Speech and language processing, Prentice Hall, 2000.
6. C.MANNING, H.SCHUTZE: Foundation of statistical natural language processing, MIT, 1999.
7. (Editor) R. MITKOV: The Oxford Handbook of Computational Linguistics, Oxford University Press, 2003.
8. S.J.RUSSELL, P.NORVIG: Artificial intelligence.A modern approach, Prentice-Hall International,1995.
9. D.TATAR: Inteligenta artificiala: demonstrare automata de teoreme, prelucrarea limbajului natural, Editura Albastra, Microinformatica, 2001.
ra Academiei, 2000, pg 289-300.
10. D. TATAR: Inteligenta artificiala. Aplicatii in prelucrarea limbajului natural,Editura Albastra, Microinformatica, 2003, ISBN 973-650-100-0

VI. Thematic of didactic activities per weeks

VII. Didactic methods used

VIII. Assessment
 (1) Project (providing a  NL Semantics techniques tool) .... 30% 

  (2) Research based on recent papers (at least 2)...30%

  (3) Final examination .............................40%

IX. Additional references

	Dean,							Chair head,

	Date,							Course responsible,
								Prof. TATAR Doina, Ph.D.