Unit 1 Introduction to NLP

6 lecture hours

• What is NLP? Why NLP is Difficult? History of NLP, Advantages of NLP, Disadvantages of NLP, Components of NLP, Applications of NLP, How to build an NLP pipeline? Phases of NLP, NLP APIs
• NLP Libraries

Unit 2 Language Modeling and Part of Speech Tagging

12 lecture hours

• Unigram Language Model
• Advanced smoothing for language modeling
• Empirical Comparison of Smoothing Techniques
• Applications of Language Modeling
• Natural Language Generation
• Parts of Speech Tagging
• Named Entity Recognition

Unit 3 Words and Word Forms

7 lecture hours

• Bag of words
• Continuous Bag-Of-Words
• Embedding representations for words Lexical Semantics
• Word Sense Disambiguation
• Knowledge Based and Supervised Word Sense Disambiguation

Unit 4 Text Analysis, Summarization and Extraction

10 lecture hours

• Sentiment Mining
• Text Classification
• Text Summarization
• Information Extraction
• Named Entity Recognition
• Relation Extraction
• Question Answering in Multilingual Setting
• NLP in Information Retrieval
• Cross-Lingual IR

Unit 5 Machine Translation

• Need of MT, Problems of Machine Translation
• MT Approaches
• Direct Machine Translations
• Rule-Based Machine Translation
• Knowledge Based MT System
• Statistical Machine Translation (SMT)
• Parameter learning in SMT (IBM models) using EM)
• Encoder-decoder architecture
• Neural Machine Translation 10 Subject Code: 3174205 w.e.f