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Natural Language Processing Course Code : 14-CS-533 Course Title : Natural Language Processing L-T-P : 3-0-0 Credits : 3 Prerequisite : Syllabus: Mathematical Foundations, Linguistic Essentials, Corpus-Based Work. Words: Collocations, Statistical Inference: n-gram Models over Sparse Data, Word Sense Disambiguation, Lexical Acquisition. Grammar: Markov Models, Part-of-Speech Tagging, Probabilistic Context Free Grammars, probabilistic parsing. Applications and Techniques: Statistical Alignment and Machine Translation, Clustering, Topics in Information Retrieval, Text Categorization. A Comprehensive Mathematical Framework for the Development of Semantic Technologies, Formal Methods and Algorithms for the Design of Semantics-Oriented Linguistic Processors. Structural Discovery in Natural Language Processing: Graph Models, Small words of Natural Language, Graph Clustering, Unsupervised Language Separation. Unsupervised Part-of- Speech Tagging, Word sense Induction and Disambiguation, Graph Based Natural Language Processing. Text Books: 1. Christopher D Manning, Hinrich Schutze, ―Foundations of Statistical Natural Language Processing‖, MIT Press, 2003. 2. Semantics-Oriented Natural Language Processing by Vladimir A. Fomichov, Springer publications References: 1. Structure Discovery in Natural Language by Chris Biemann, Springer publications 2. Graph-based Natural Language Processing and Information Retrieval by Rada Mihalcea, Dragomir Radev, Cambridge Publications 3. Lucja M Iwanska, Stuart C Shapiro, ―Natural Language Processing And Knowledge Representation: Language For Knowledge And Knowledge For Language‖, AAAI Press, 2000. 4. Anne Kao, Stephen R Poteet, ―Natural Language Processing and Text Mining‖, Springer, 2010. 5. Daniel Jurafsky, James H Martin, ―Speech and Language Procesing‖, Pearson, 2000 6. James Allen, ―Natural Language Understanding‖, 2nd Edition, Pearson, 2008.
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