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CSC3205: COMPUTATIONAL LINGUISTICS

Core/Elective: Elective Semester: 2 Credits: 3

Course Description

Computational Linguistics deals with statistical and rule based modelling of natural languages from a computational point of view. This course is intended to give a comprehensive coverage of speech and language processing fundamentals like Phonology, morphology, Syntax, Semantics and pragmatics. Application of various computational models in application domains like Machine translation, information retrieval, speech generation and recognition are also dealt with.

Course Objectives

To familiarise the fundamentals of speech and written language processing
To study the applications of these techniques in real world problems like spell-checking, Parts-of Speech Tagging, Corpus development, Wordnet, speech recognition, pronunciation modelling, dialogue agents, document retrieval etc
To gather information about widely used language processing resources

Course Content

1. Words- Regular Expressions and Finite Automata-Morphology and Finite State Transducers-Probabilistic Models of Pronunciation and Spelling

2. Syntax-N grams Models of Syntax-HMMs and Speech Recognition-Word Classes and Part-of-Speech Tagging-Context Free Grammars for English Syntax-Parsing with Context Free Grammars- Features and Unification-Lexicalized and Probabilistic Parsing-Language and Complexity

3. Semantics-Representing Meaning-Semantic Analysis-Lexical Semantics-Word Sense Disambiguation and Information Retrieval

4. Discourse-Reference Resolution -Text Coherence -Dialog and Conversational Agents-Language Generation

5. Multilingual Processing- Machine Translation-Language Similarities and Differences-The Transfer Metaphor -The Interlingua Idea: Using Meaning -Direct Translation Using Statistical Techniques -Usability and System Development

REFERNCES

1. Speech and Language Processing (1st Ed): Jurafsky and Martin, PH (2000)
2. Statistical Natural Language Processing (1st Ed): Manning and Schutze, MIT Press (1999)
3. Natural Language Understanding (1st Ed): James Allen, The Benajmins/Cummings Publishing Company Inc. (1994)
4. Machine Learning (1st Ed): Tom Mitchell, McGraw Hill (1997)
5. Elements of Information Theory (1st Ed): Cover, T. M. and J. A. Thomas, Wiley (1991)
6. Statistical Language Learning (1st Ed): Charniak E., MIT Press (1996)
7. Statistical Methods for Speech Recognition (1st Ed): Jelinek, F., MIT Press (1998)


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