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
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
Semantic Parsing:
Introduction, Semantic Interpretation, System Paradigms, Word Sense Systems, Software.
Predicate-Argument Structure, Meaning Representation Systems, Software.
Discourse Processing:
Cohension, Reference Resolution, Discourse Cohension 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 Crosslingual 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
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
5. Able to design different language modeling Techniques.