JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD

NATURAL LANGUAGE PROCESSING

M.Tech I Year I Semester (R22)

Prerequisites:

  • 1. Data structures, finite automata and probability theory.

Course Objectives:

  • 1. Introduction to some of the problems and solutions of NLP and their relation to linguistics and statistics.

Course Outcomes:

  • 1. Show sensitivity to linguistic phenomena and an ability to model them with formal grammars.
  • 2. Understand and carry out proper experimental methodology for training and evaluating empirical NLP systems
  • 3. Able to manipulate probabilities, construct statistical models over strings and trees, and estimate parameters using supervised and unsupervised training methods.
  • 4. Able to design, implement, and analyze NLP algorithms Able to design different language modeling Techniques.
  • 5. Able to design different language modeling Techniques.

UNIT - I

Finding the Structure of Words: Words and Their Components, Issues and Challenges, Morphological Models. Finding the Structure of Documents: Introduction, Methods, Complexity of the Approaches, Performances of the Approaches

UNIT - II

Syntax Analysis: Parsing Natural Language, Treebanks: A Data-Driven Approach to Syntax, Representation of Syntactic Structure, Parsing Algorithms, Models for Ambiguity Resolution in Parsing, Multilingual Issues.

UNIT - III

Semantic Parsing: Introduction, Semantic Interpretation, System Paradigms, Word Sense Systems, Software.

UNIT - IV

Predicate-Argument Structure, Meaning Representation Systems, Software.

UNIT - V

Discourse Processing: Cohesion, Reference Resolution, Discourse Cohesion and Structure Language Modeling: Introduction, N-Gram Models, Language Model Evaluation, Parameter Estimation, Language Model Adaptation, Types of Language Models, Language-Specific Modeling Problems, Multilingual and Cross Lingual Language Modeling.

TEXT BOOKS:

  • 1. Multilingual natural Language Processing Applications: From Theory to Practice – Daniel M. Bikel and Imed Zitouni, Pearson Publication
  • 2. Natural Language Processing and Information Retrieval: Tanvier Siddiqui, U.S. Tiwary

REFERENCE:

  • 1. Speech and Natural Language Processing - Daniel Jurafsky & James H Martin, Pearson Publications

Loading Question Papers

Fetching data from database...