Syllabus - Artificial Intelligence and Machine Learning (AL 504 (B))

CSE

Artificial Intelligence and Machine Learning (AL 504 (B))

V-Semester

UNIT I

Introduction

Origins and challenges of NLP – Language Modeling: Grammar-based LM, Statistical LM – Regular Expressions, Finite-State Automata – English Morphology, Transducers for lexicon and rules, Tokenization, Detecting and Correcting Spelling Errors, Minimum Edit Distance.

UNIT II

Word Level Analysis

Unsmoothed N-grams, Evaluating N-grams, Smoothing, Interpolation and Backoff – Word Classes, Part-of-Speech Tagging, Rule-based, Stochastic and Transformation-based tagging, Issues in PoS tagging – Hidden Markov and Maximum Entropy models, Viterbi algorithms and EM training.

UNIT III

Syntactic Analysis

Context-Free Grammars, Grammar rules for English, Treebanks, Normal Forms for grammar – Dependency Grammar – Syntactic Parsing, Ambiguity, Dynamic Programming parsing – Shallow parsing – Probabilistic CFG, Probabilistic CYK, Probabilistic Lexicalized CFGs – Feature structures, Unification of feature structures.

UNIT IV

Semantics and Pragmatics

Requirements for representation, First-Order Logic, Description Logics – Syntax-Driven Semantic analysis, Semantic attachments – Word Senses, Relations between Senses, Thematic Roles, selectional restrictions – Word Sense Disambiguation, WSD using Supervised, Dictionary & Thesaurus, Bootstrapping methods – Word Similarity using Thesaurus and Distributional methods.Compositional semantics.

UNIT V

Application of NLP

intelligent work processors: Machine translation, user interfaces, Man-Machine interfaces, natural language querying, tutoring and authoring systems, speech recognition, and commercial use of NLP.

Course Objective

Students should develop a basic understanding in natural language processing methods and strategies and to evaluate the strengths and weaknesses of various Natural Language Processing (NLP) methods & technologies and gain an insight into the application areas of Natural language processing.

Course Outcome

["Define different data models used in Information Retrieval using NLP.", "Demonstrate current methods for statistical approaches to machine translation.", "Apply syntactic parsing and semantic analysis on text.", "Solve and implement real world problems using NLP."]

Practicals

Reference Books

  • Daniel Jurafsky, James H. Martin―Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics and Speech, Pearson Publication.

  • Steven Bird, Ewan Klein and Edward Loper, ―Natural Language Processing with Python, OReilly Media.

  • Manning and Schutze "Foundations of Statistical Natural Language Processing", MIT Press.

  • Breck Baldwin, Language Processing with Java and LingPipe Cookbook, Atlantic Publisher.

  • Richard M Reese, Natural Language Processing with Java, OReilly Media.

  • Nitin Indurkhya and Fred J. Damerau, Handbook of Natural Language Processing, Chapman and Hall/CRC Press.

  • Tanveer Siddiqui, U.S. Tiwary, Natural Language Processing and Information Retrieval, Oxford University Press.