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	Category	Status	
2	2+1+0	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 problems of 
automated generation and understanding 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. Unification grammars. Feature structures as graphs, AVM and descriptors. 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’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, ISBN 973-650-100-0	

VI. Thematic of didactic activities per weeks
1. Introduction in Natural Language Processing (NLP): Stages, Domains, Chapters: 1 lecture
 2. Corpora:1 lecture 
 3. Word Sense Disambiguation.
     - Machine learning approach: supervised (NBC  and k-NN): 1 lecture  
      -  unsupervised (by clustering):1 lecture 
      -  Dictionary based approach (Lesk, Yarowsky, bilingual dictionaries): 1 lecture 
 4. Description logics for natural language processing: 2 lectures
 5. Statistics in  NLP: 
       - Markov chains, Hidden Markov Model: 1 lecture .
        - Evaluation, Estimation and Training with HMM. The probability of input sequences, the most likely path: 1 lecture 
        - Applications to POS tagging: 1 lecture  .
 6. Probabilistic Context- free Grammars.  Syntactic analysis: 1 lecture  
 7. Unification grammars. 
       - Feature structures as objects of linguistic knowledge representation. Feature structures as graphs, AVM and descriptors: 1 lecture 
       - Subsumation and unification. Proof theory of descriptors. Well-typed and total-well-typed feature structures: 1 lecture
        - Parsing  with unification grammars (top-down and bottom-up): 1 lecture 	

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 stimulation of editing papers for conferences. 	

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,

          1.10.2009	      			                   Lect. LUPEA Mihaiela, Ph.D.