NATURAL LANGUAGE PROCESSING (18CS743)

NATURAL LANGUAGE PROCESSING

Course Code:18CS743
CIE Marks:40
Number of Contact Hours/Week:3:0:0
SEE Marks:60
Total Number of Contact Hours:40
Exam Hours:03
CREDITS –3

Module – 1

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.
Textbook 1: Ch. 1,2
RBT: L1, L2, L3

Notes will be uploaded soon

Module – 2

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.
Textbook 1: Ch. 3,4
RBT: L1, L2, L3

Notes will be uploaded soon

Module – 3 

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.
Textbook 2: Ch. 3,4,5
RBT: L1, L2, L3

Notes will be uploaded soon

Module – 4

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.
Textbook 2: Ch. 6,7,8,9
RBT: L1, L2, L3

Notes will be uploaded soon

Module – 5

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.
Textbook 1: Ch. 9,12
RBT: L1, L2, L3

Notes will be uploaded soon

Course outcomes: The students should be able to: 

• Analyze the natural language text. 
• Define the importance of natural language. 
• Understand the concepts of Text mining.
• Illustrate information retrieval techniques.

Question paper pattern:

 The question paper will have ten questions.
 There will be 2 questions from each module.
 Each question will have questions covering all the topics under a module.
 The students will have to answer 5 full questions, selecting one full question from each module.

Text Books: 

1. Tanveer Siddiqui, U.S. Tiwary, “Natural Language Processing and Information Retrieval”, Oxford University Press, 2008. 
2. Anne Kao and Stephen R. Poteet (Eds), “Natural language processing and Text Mining”, Springer-Verlag London Limited 2007.

Reference Books: 

1. Daniel Jurafsky and James H Martin, “Speech and Language Processing: An introduction to Natural Language Processing, Computational Linguistics and SpeechRecognition”, 2nd Edition, Prentice-Hall, 2008. 2. James Allen, “Natural Language Understanding”, 2nd edition, Benjamin/Cummingspublishing company, 1995. 3. Gerald J. Kowalski and Mark.T. Maybury, “Information Storage and Retrieval systems”, Kluwer Academic Publishers, 2000.