Natural Language Processing
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Follow on Instagram for UpdatesNatural Language Processing Syllabus — Unit-wise Content
The official GTU curriculum for Natural Language Processing (subject code 3164902) as prescribed for CS-Design · Semester 6 divides the course into 5 units, covering 32 listed topics. The unit-wise breakdown below is read from the GTU syllabus document for this subject code, so you can check what the course actually covers before deciding which papers to attempt.
Download the official syllabus PDF (3164902)Unit 1 Introduction to NLP
- 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
- 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
- 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
- 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
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