Unit 1 Introduction

3 lecture hours

• Introduction to NLP
• NLP tasks in syntax
• semantics, and pragmatics
• Applications such as information extraction
• question answering, and machine translation
• The problem of ambiguity
• The role of machine learning
• Brief history of the field. w. e. f
• Academic Year: A.Y. 2024-25 Semester: 2 Category of the Course: PCC Program Name: Master of Engineering Level: PG Branch: Information Technology Subject Code : ME02023011 Course / Subject Name : Natural Language Processing

Unit 2 N-gram Language Models

10 lecture hours

• N-grams Models of Syntax - Counting Words - Unsmoothed N-grams
• Smoothing- Back-off Deleted Interpolation – Entropy - English Word Classes - Tag sets for English Part of Speech Tagging-Rule Based Part of Speech Tagging - Stochastic Part of Speech Tagging - Transformation-Based Tagging

Unit 3 Context Free Grammars

7 lecture hours

• Context Free Grammars for English Syntax- Context-Free Rules and Trees -Understand the network simulation tools
• Sentence- Level Constructions–Agreement – Sub Categorization .Parsing – Top-down – Early Parsing -feature Structures – Probabilistic Context-Free Grammars

Unit 4 Semantic Analysis

7 lecture hours

• Representing Meaning-Meaning Structure of Language-First Order Predicate Calculus Representing Linguistically Relevant Concepts -Syntax-Driven Semantic Analysis - Semantic Attachments -Syntax-Driven Analyzer
• Robust Analysis - Lexemes and Their Senses - Internal Structure - Word Sense Disambiguation - Information Retrieval

Unit 5 Language Generation And Discourse Analysis

7 lecture hours

• Discourse - Reference Resolution - Text Coherence -Discourse Structure – Coherence
• Dialog and Conversational Agents - Dialog Acts – Interpretation -Conversational Agents
• Language Generation– Architecture-Surface Realizations - Discourse Planning .Machine Translation -Transfer Metaphor– Interlingua – Statistical Approaches

Unit 6 Information Extraction (IE)

• Named entity recognition and relation extraction
• IE using sequence labeling. 3 10

Unit 7 Machine Translation (MT)

4 lecture hours

• Basic issues in MT
• Statistical translation
• word alignment
• phrase- based translation, and synchronous grammars

Unit 8 Applications of NLP

• Sentiment Analysis
• Text Summarization