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	
MI282	Natural Language Processing	

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

II. Full status faculty members
Name and	Scientific	Didactic	Chair	Type of activity	
surname	title	title		C	S	L	
LUPSA Dana	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
Natural language processing is now accepted as one of the most studied and active field of Computer Science. The notion of feature structure as linguistic object stands on the base of most recent approaches which are surveyed in this course. The optimization of the search on Web, the interfaces in natural language and the aspects of text mining are only some of  motivations for studying natural language processing.	

IV. Course contents
1. Introduction in Natural Language Processing: Stages, Domains, Chapters. Corpora. 
 2. Word Sense Disambiguation. Machine learning approach: supervised (NBC  and k-NN) and  unsupervised (by clustering). Dictionary based approach (Lesk, Yarowsky, bilingual dictionaries).
 3. Statistics in NLP: Markov chains, Hidden Markov Model. Evaluation, Estimation and Training with HMM. The probability of input sequences, the most likely path. Applications to POS tagging.
 4. Probabilistic Context- free Grammars.  Syntactic analysis: active charts. Earley@s algorithm.   
 5. Unification grammars. Feature structures as objects of linguistic knowledge representation. Feature structures as graphs, AVM and descriptors. Parsing  with unification grammars. 

Week 1
    Introduction in Natural Language Processing: Stages, Domains, Corpora
Week 2 & 3
    Properties of words: internal structure; morphologic aspects; semantic aspects
Week 4
    Statistics in NLP. Introduction
Week 5
    Markov chains, Hidden Markov Model. Evaluation, Estimation and Training with HMM.
Week 6
    The probability of input sequences, the most likely path. Applications to POS tagging
Week 7
    Probabilistic Context- free Grammars.
Week 8
    Syntactic analysis: active charts
Week 9 & 10
      Feature structures as objects of linguistic knowledge representation. Feature structures as graphs, AVM and descriptors.
Week 11
      Unification grammars. Parsing  with unification grammars.
Week 12
     Word Sense Disambiguation. Unsupervised machine learning (by clustering)
Week 13
     Word Sense Disambiguation. Machine learning approach: supervised (NBC  and k-NN). 
Week 14
    Word Sense Disambiguation. Dictionary based approach (Lesk, Yarowsky, bilingual dictionaries).

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.JURAFSKY, J.MARTIN: Speech and language processing, Prentice Hall, 2000.
5. C.MANNING, H.SCHUTZE: Foundation of statistical natural language processing, MIT, 1999.
6. S.J.RUSSELL, P.NORVIG: Artificial intelligence.A modern approach, Prentice-Hall International,1995.
7. D.TATAR: Inteligenta artificiala: demonstrare automata de teoreme, prelucrarea limbajului natural, Editura Albastra, Microinformatica, 2001.
8. D.TATAR: Unification Grammars in Natural Language Processing, in "Recent topics in mathematical and computational linguistic, ed. C. Martin-Vide, G. Paun, Editura Academiei, 2000, pg 289-300.
9. D. TATAR: Inteligenta artificiala. Aplicatii in prelucrarea limbajului natural,Editura Albastra, Microinformatica, 2003, ISBN 973-650-100-0
10. Editor R. MITKOV: The Oxford Handbook of Computational Linguistics, Oxford University Press, 2003.	

VI. Thematic of didactic activities per weeks

VII. Didactic methods used
Expositions: description, explanation, class lectures, dialog-based lectures, lectures with demonstrations 
Conversations: debate, dialog, introductive conversations, conversations for knowledge consolidation, 
Use of problems: use of problem questions, problems and problem situations. 
Discovery: directed and independent rediscovery, creative discovery, inductive discovery, deductive discovery, discovery by documenting, experimental discovery. 
Other methods: case study; exercise, homework study.

VIII. Assessment
The examination is by written  exam, with the subjects from all the matter (60%). Will be evaluated the activity of understanding and communication of some recent papers in the field and implementation of some apllications (40%).	

IX. Additional references

	Dean,							Chair head,

	Date,							Course responsible,
								Lect. LUPSA Dana, Ph.D.