BCS714B | NATURAL LANGUAGE PROCESSING

Module 1
Introduction: What is Natural Language Processing? Origins of NLP, Language and Knowledge, The Challenges of NLP, Language and Grammar, Processing Indian Languages, NLP Applications.
Language Modeling: Statistical Language Model – N-gram model (unigram, bigram), Paninion Framework, Karaka theory.
Module 2
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, Top-down and Bottom-up Parsing, CYK Parsing.
Module 3
Naive Bayes, Text Classification and Sentiment: Naive Bayes Classifiers, Training the Naive Bayes Classifier, Worked Example, Optimizing for Sentiment Analysis, Naive Bayes for Other Text Classification Tasks, Naive Bayes as a Language Model.
Module 4
Information Retrieval: Design Features of Information Retrieval Systems, Information Retrieval Models – Classical, Non-classical, Alternative Models of Information Retrieval – Custer model, Fuzzy model, LSTM model, Major Issues in Information Retrieval. Lexical Resources: WordNet, FrameNet, Stemmers, Parts-of-Speech Tagger, Research Corpora.
Module 5
Machine Translation: Language Divergences and Typology, Machine Translation using Encoder-Decoder, Details of the Encoder-Decoder Model, Translating in Low-Resource Situations, MT Evaluation, Bias and Ethical Issues.

BCS714B | Model Question Paper with Solution

MQP Solution
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BCS714B | Passing Package
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Passing Package
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