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

SUBJECT

Code
Subject
MI070 Natural Language Processing
Section
Semester
Hours: C+S+L
Category
Type
Computer Science - in Romanian
7
2+0+2
optional
Mathematics-Computer Science - in Romanian
7
2+0+2
optional
Teaching Staff in Charge
Lect. LUPSA Dana, Ph.D.,  danacs.ubbcluj.ro
Aims
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. Also, are presented the semantics and pragmatics of natural language processing which are central in human -computer interaction, information retrieval, text mining, text summarization and text generation.
Content
1. Feature structures (FS) as objects of linguistic knowledge representation. Subsumation and unification of FSs. Descriptors and FS. Variables and logic programming. Well-typed and total-well-typed feature structures.
2. Unification grammars, definite clause grammars, PATR, HPSG grammars. Rules for rewriting. ALE as soft for HPSG grammars.
3. Statistic methods for natural language processing. Hidden Markov Model (HMM). The probability of input sequences. Probabilistic grammars.
4. Semantic disambiguation: algorithms for supervised, bootstrapping and non-supervised disambiguation. Application to text categorization, text summarization and machine translation.
References
1. J.ALLEN : "Natural language understanding", Benjamin/Cummings Publ., 2nd ed., 1995.
2. B.CARPENTER: "The logic of typed feature structures", Cambridge University Press,1992.
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. G.MORILL:"Type LogicalGrammar.Categorial Logic of Signs", Kluwer Academic Publishers, 1994.
7. S.J.RUSSELL, P.NORVIG: "Artificial intelligence.A modern approach", Prentice-Hall International, 1995.
8. D.TATAR: "Inteligenta artificiala: demonstrare automata de teoreme, prelucrarea limbajului natural", Editura Albastra, Microinformatica, 2001.
9. D.TATAR: "Inteligenta artificiala. Aplicatii in prelucrarea limbajului natural", Ed. Albastra, Microinformatica, 2003
10.E. CHARNIAK: "Statistical language learning", MIT Press, 1996.
Assessment
The examination is by oral exam (70%). The activity of developing a project at laboratory will be a component of the final mark (30%).
Links: Syllabus for all subjects
Romanian version for this subject
Rtf format for this subject