CS2035 NATURAL LANGUAGE PROCESSING

CS2035 NATURAL LANGUAGE PROCESSING L T P C
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3 0 0 3
UNIT I 9
Introduction – Models -and Algorithms - The Turing Test -Regular Expressions
Basic Regular Expression Patterns -Finite State Automata -Regular Languages and
FSAs – Morphology -Inflectional Morphology - Derivational Morphology -Finite-State
Morphological Parsing - Combining an FST Lexicon and Rules -Porter Stemmer
UNIT II 9
N-grams Models of Syntax - Counting Words - Unsmoothed N-grams – Smoothing-
Backoff - Deleted Interpolation – Entropy - English Word Classes - Tagsets for English -
Part of Speech Tagging -Rule-Based Part of Speech Tagging - Stochastic Part of
Speech Tagging - Transformation-Based Tagging -
UNIT III 9
Context Free Grammars for English Syntax- Context-Free Rules and Trees - Sentence-
Level Constructions –Agreement – Sub Categorization – Parsing – Top-down – Earley
Parsing -Feature Structures - Probabilistic Context-Free Grammars
UNIT IV 9
Representing Meaning - Meaning Structure of Language - First Order Predicate Calculus
- Representing Linguistically Relevant Concepts -Syntax-Driven Semantic Analysis -
Semantic Attachments - Syntax-Driven Analyzer - Robust Analysis - Lexemes and Their
Senses - Internal Structure - Word Sense Disambiguation -Information Retrieval
UNIT V 9
Discourse -Reference Resolution - Text Coherence -Discourse Structure - Dialog and
Conversational Agents - Dialog Acts – Interpretation – Coherence -Conversational
Agents - Language Generation – Architecture -Surface Realizations - Discourse
Planning – Machine Translation -Transfer Metaphor – Interlingua – Statistical
Approaches
TOTAL: 45 PERIODS
TEXT BOOKS:
1. D. Jurafsky and J. Martin “Speech and Language Processing: An Introduction to
Natural Language Processing, Computational Linguistics, and Speech Recognition”,
2. C. Manning and H. Schutze, “Foundations of Statistical Natural Language
Processing”,
REFERENCE:
1. James Allen. “Natural Language Understanding”, Addison Wesley, 1994.