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	Knowledge Based Systems and Language Technology	

Semester	Hours: C+S+L+P	Category	Status	
2	2+1+0+1	speciality	compulsory	

II. Full status faculty members
Name and	Scientific	Didactic	Chair	Type of activity	
surname	title	title		C	S	L	
LUPEA Mihaiela	Ph.D.	Lect.	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 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.	

IV. Course contents
 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. Description logics for natural language processing. 
 6. Statistical natural language processing  and LT: Markov chains, Hidden Markov Model. 
    Evaluation, Estimation and Training with HMM.  Applications.
 7. Probabilistic Context- free Grammars.  Syntactic analysis: active charts.  
 8. Feature structures (FS):  FS as graphs, AVM and descriptors. Unification of Feature 
    structures. 
 9. Unification grammars. 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.FEHRER et al: Description logics for natural language processing. In Proc. of the 
   1994 Description Logic Workshop (DL&#8217;94), 1994.
5. H. Helbig: $Knowledge Representation and the Semantics of Natural Language$, Springer, 
   2006. 
6. D.JURAFSKY, J.MARTIN: Speech and language processing, Prentice Hall, 2000.
7. C.MANNING, H.SCHUTZE: Foundation of statistical natural language processing, MIT, 1999.
8. (Editor) R. MITKOV: The Oxford Handbook of Computational Linguistics, Oxford  
   University Press, 2003.
9. D.TATAR: Inteligenta artificiala: demonstrare automata de teoreme, prelucrarea 
   limbajului natural, Editura Albastra, Microinformatica, 2001.
10. D. TATAR: Inteligenta artificiala. Aplicatii in prelucrarea limbajului natural, 
   Editura Albastra, Microinformatica, 2003.

VI. Thematic of didactic activities per weeks

VII. Didactic methods used

VIII. 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%

IX. Additional references

	Dean,							Chair head,

	Date,							Course responsible,
								Lect. LUPEA Mihaiela, Ph.D.