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 hasas 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. 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. 
 9. Description logics for natural language processing.	

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 973-650-100-01. J.ALLEN : Natural language understanding, Benjamin/Cummings Publ., 2nd ed., 1995. 	

VI. Thematic of didactic activities per weeks

1. Introduction in Natural Language Processing (NLP): Stages, Domains, Chapters – 1 lecture.

2. WordNet:  knowledge structure, semantic relations, lexical relations, applications, interfaces; Corpora-1 lecture. 

3.  Syntactic parsing - 2 lectures
     - grammar rules for English - sentence level construction;
     - Cocke-Kasami-Yonger (CKY) algorithm;
     -  Earley algorithm;
     - chart parsing approach.

 4.  Statistical parsing – 1 lecture
     - Probabilistic Context-Free Grammars (PCFG) ;
     - Probabilistic CKY (Cocke-Kasami-Yonger) parsing of PCFGs. 

5. Statistics in  NLP- 3 lectures
       - Markov chains, Hidden Markov Model;
       - three canonical problems associated with HMM and the corresponding algorithms 
         for solving them: the forward algorithm; the Viterbi algorithm, the Baum-Welch algorithm;
      - applications to POS tagging.
6. Word Sense Disambiguation- 3 lectures
      - machine learning approach: supervised (NBC  and k-NN);  
      -  unsupervised (by clustering);
      -  dictionary based approach (Lesk, Yarowsky, bilingual dictionaries).

7. Feature structures in NLP- 2 lectures
        - feature structures as objects of linguistic knowledge representation, feature structures 
          as graphs, AVM and descriptors, subsumation and unification;
-  feature structures in the unification grammars, applications of unification constraints
   to four interesting linguistic phenomena: agreement phenomena, grammatical heads
   associated to syntactic constituents; subcategorization of verbs; long-distance dependencies;
      -  parsing  with unification grammars. Integration of unification into an Earley parser.

 8. Description logics for natural language processing - 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,
	1.10.2010						Lect. LUPEA Mihaiela, Ph.D.