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

Code	Subject	
MII1005	Knowledge Based Systems and Language Technology	

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

Teaching Staff in Charge	Prof. TATAR Doina, Ph.D., dtatar@cs.ubbcluj.ro	
Aims	
Language technology (LT) is a subfield of  artificial intelligence which  studies the quantitative aspects of natural human languages. The main tools in the applications of LT are Knowledge Based Systems (KBS), as for example ontologies, thesauri, corpora, electronical dictionaries.  The course has as objectives to provide the basic 
principles, technologies and applications of  LT and KBS . The optimization of the search on Web, the interfaces in natural language and the recent  aspects of text mining are only some of the  motivations for studying LT and KBS.  A deep  understanding of the current state of the art in LT  in order to realize  original research in LT is another 
goal of this course.	
Content	 1. Language Technology: Stages, Domains, Chapters. 
 2. The corpus as a KBS and as a tool of different applications in  LT. The most important taggs for an annotated corpus. Using the corpus in a  supervised  method. 
 3. WordNet as a KBS. Relations and synsets. Examples of applications.  
 4. Word Sense Disambiguation(WSD): machine learning approach (supervised and unsupervised), dictionary based approach. Where WSD is needed.  
 5. Statistical natural language processing  and LT: Markov chains, Hidden Markov Model. Evaluation, Estimation and Training with HMM.  Applications.
 6. Probabilistic Context- free Grammars.  Syntactic analysis: active charts.  
 7. Feature structures (FS):  FS as graphs, AVM and descriptors. Unification of Feature structures. 
 8. Unification grammars. Parsing  with Unification grammars. 

	
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	The final note will have the following components :  

  (1) Project (providing a  LT or KBS techniques tool) .... 30% 

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

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