Natural Language Processing
Below is the syllabus for Natural Language Processing:-
Fundamental components of Natural Language Processing: Lexicography, syntax, semantics, prosody, phonology, pragmatic analysis, world knowledge.
Knowledge Representation schemes: Semantic net, Frames, Conceptual Dependency, Scripts.
Representing knowledge using rules: Logic Programming, Introduction to LISP and Prolog, Rules-based deduction systems, General concepts in knowledge acquisition.
Syntax Analysis: Formal Languages and grammars, Chomsky Hierarchy, Left- Associative Grammars, ambiguous grammars, resolution of ambiguities.
Computation Linguistics: Recognition and parsing of natural language structures- ATN and RTN, General Techniques of parsing- CKY, Earley and Tomita’s algorithm.
Semantics: Knowledge representation, semantics networks logic and inference pragmatics, graph models, and optimization.
Applications of NLP: Intelligent work processor, Machine translation, user interfaces, Man-Machine interfaces, natural language querying, tutoring and authoring systems, speech recognition, commercial use of NLP.
- Daniel Jurafsky, James H. Martin, “Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics and Speech Recognition”, 2nd edition, Pearson Edu.,
- James Allen, “Natural Language Understanding”, Pearson Education, Second Edition,
- Ivan Bratko, “Prolog: Programming for Artificial Intelligence”, 3rd Edition, Pearson Education, Fifth Impression
- Gazder, “Natural Language processing in prolog”, Addison Wesley, 1989.
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