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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 language processing fundamentals like morphology, Syntax, Semantics and pragmatics. Application of various computational models in application domains like Machine translation, information retrieval etc. is 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 -N grams

2. Word Classes and Part-of-Speech Tagging-MM Taggers- probabilistic Context Free Grammars for English Syntax-Parsing with Context Free Grammars- probabilistic parsing- Features and Unification-Language and Complexity

3. Semantics-Representing Meaning-canonical forms-FOPC-ambiguity resolution-scoping phenomena-Semantic Analysis-syntax driven semantic analysis-Lexical Semantics-Word Sense Disambiguation and Information Retrieval

4. Discourse-Reference Resolution -Text Coherence -Dialog and Conversational Agents-Dialogue acts-dialogue structure

5. Statistical alignment and machine translation-clustering- text categorization


1. Foundations of statistical natural language processing (1st Ed): Christopher D. Manning and HinRich Schutze, MIT press (1999)
2. Speech and Language Processing (2nd Ed): Daniel Jurafsky and James Martin, PH (2008)
3. Natural Language Understanding (2nd Ed): James Allen, The Benajmins/Cummings Publishing Company Inc. (1994)

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