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Natural Language Processing 21AI643

Natural Language Processing 21AI643

Natural Language Processing 21AI643

Course Code: 21AI643

Credits: 03

CIE Marks: 50

SEE Marks: 50

Total Marks: 100

Exam Hours: 03

Total Hours of Pedagogy: 40T

Teaching Hours/Weeks: [L:T:P:S] 3:0:0:0

Overview and language modeling: Overview: Origins and challenges of NLP-Language and Grammar- Processing Indian Languages- NLP Applications-Information Retrieval.

Language Modeling: Various Grammar-based Language Models-Statistical Language Model.

Word level and syntactic analysis: Word Level Analysis: Regular Expressions-Finite-State Automata- Morphological Parsing-Spelling Error Detection and correction-Words and Word classes-Part-of Speech Tagging.

Syntactic Analysis: Context-free Grammar-Constituency- Parsing-Probabilistic Parsing.

Extracting Relations from Text: From Word Sequences to Dependency Paths: Introduction, Subsequence Kernels for Relation Extraction, A Dependency-Path Kernel for Relation Extraction and Experimental Evaluation.

Mining Diagnostic Text Reports by Learning to Annotate Knowledge Roles: Introduction, Domain Knowledge and Knowledge Roles, Frame Semantics and Semantic Role Labeling, Learning to Annotate Cases with Knowledge Roles and Evaluations.

A Case Study in Natural Language Based Web Search: InFact System Overview, The GlobalSecurity.org Experience.

Evaluating Self-Explanations in iSTART: Word Matching, Latent Semantic Analysis, and Topic Models: Introduction, iSTART: Feedback Systems, iSTART: Evaluation of Feedback Systems.

Textual Signatures: Identifying Text-Types Using Latent Semantic Analysis to Measure the Cohesion of Text Structures: Introduction, Cohesion, Coh-Metrix, Approaches to Analyzing Texts, Latent Semantic Analysis, Predictions, Results of Experiments.

Automatic Document Separation: A Combination of Probabilistic Classification and Finite-State Sequence Modeling: Introduction, Related Work, Data Preparation, Document Separation as a Sequence Mapping Problem, Results.

Evolving Explanatory Novel Patterns for Semantically-Based Text Mining: Related Work, A Semantically Guided Model for Effective Text Mining.

Information Retrieval and Lexical Resources: Information Retrieval: Design features of Information Retrieval Systems-Classical, Non classical, Alternative Models of Information Retrieval – valuation Lexical Resources: World Net-Frame Net- Stemmers-POS Tagger- Research Corpora.

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2021 SCHEME QUESTION PAPER

Regular Paper

Model Paper

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