Babes-Bolyai University of Cluj-Napoca
Faculty of Mathematics and Computer Science
Study Cycle: Master
SUBJECT

Code	Subject	
MII1008	Computational Approaches for  Natural Language Semantics	

Section	Sem.	Hours: C+S+L	Category	Type	
Intelligent Systems - in English	4	2+1+0	speciality	compulsory	

Teaching Staff in Charge	Prof. TATAR Doina, Ph.D., dtatar@cs.ubbcluj.ro	
Aims	      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.

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

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

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