CS 802F-Natural Language Processing |
Introduction to NLP |
Definition, issues and strategies, application domain, tools for NLP, Linguistic organisation of NLP, NLP vs PLP.
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Morphology: |
Inflectional, derivational, parsing and parsing with FST, Combinational Rules |
Phonology: |
Speech sounds, phonetic transcription, phoneme and phonological rules, optimality theory, machine learning of
phonological rules, phonological aspects of prosody and speech synthesis.
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Pronunciation, Spelling and N-grams: |
Spelling errors, detection and elimination using probabilistic models, pronunciation
variation (lexical, allophonic, dialect), decision tree model, counting words in Corpora, simple N-grams, smoothing (Add
One, Written-Bell, Good-Turing), N-grams for spelling and pronunciation.
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Syntax |
POS Tagging: Tagsets, concept of HMM tagger, rule based and stochastic POST, algorithm for HMM tagging,
transformation based tagging
Sentence level construction & unification: Noun phrase, co-ordination, sub-categorization, concept of feature structure
and unification. |
Semantics |
Representing Meaning: Unambiguous representation, canonical form, expressiveness, meaning structure of language,
basics of FOPC
Semantic Analysis: Syntax driven, attachment & integration, robustness
Lexical Semantics: Lexemes (homonymy, polysemy, synonymy, hyponymy), WordNet, internal structure of words,
metaphor and metonymy and their computational approaches
Word Sense Disambiguation: Selectional restriction based, machine learning based and dictionary based approaches.
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Pragmatics |
Discourse: Reference resolution and phenomena, syntactic and semantic constraints on Coreference, pronoun resolution
algorithm, text coherence, discourse structure
Dialogues: Turns and utterances, grounding, dialogue acts and structures
Natural Language Generation: Introduction to language generation, architecture, dicourse planning (text schemata,
rhetorical relations).
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Text Books: |
1. D. Jurafsky & J. H. Martin – “Speech and Language Processing – An introduction to Language processing,
Computational Linguistics, and Speech Recognition”, Pearson Education
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Text Books: |
1. Allen, James. 1995. – “Natural Language Understanding”. Benjamin/Cummings, 2ed.
2. Bharathi, A., Vineet Chaitanya and Rajeev Sangal. 1995. Natural Language
Processing- “A Pananian Perspective”. Prentice Hll India, Eastern Economy Edition.
3. Eugene Cherniak: “Statistical Language Learning”, MIT Press, 1993.
4. Manning, Christopher and Heinrich Schütze. 1999. “Foundations of Statistical Natural
Language Processing”. MIT Press.
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