Natural Language Processing CS6314- NIT Rourkela

Prerequisites

• Theory: Linear Algebra, Analytic Geometry, Matrix decompositions, Vector Calculus, Probability and Distributions, Continuous Optimization. Refer Chapters 1-7 from the book `https://mml-book.github.io/book/mml-book.pdf`
• Programming: Python, Source: https://www.w3schools.com/python/default.asp,
  Pytorch\tensorflow, Source: https://pytorch.org/assets/deep-learning/Deep-Learning-with-PyTorch.pdf, https://github.com/gigwegbe/Learning-TensorFlow, TF book
• Software and Packages: Conda, Jupyter notebook, NLTK - https://www.nltk.org/book/

Books

The Majority of the course content follows from the first two references.

• Jacob Eisenstein. Natural Language Processing.
• Dan Jurafsky and James H. Martin. Speech and Language Processing (3rd ed. draft).
• Christopher Manning and Hinrich Schutze. Foundations of Statistical Natural Language Processing.

Tentative Schedule

Class	Topic	Reading	Notebooks and Assignments
1-2	Introduction, Mathematical Preliminaries	Advances in natural language processing. Chapters 1-7 from the book `https://mml-book.github.io/book/mml-book.pdf`
3-4	Basic Text Processing, Edit distance
5	Linear Text Classification
6	Word Embeddings
7-8	Language models, spelling correction
9-10	Neural Networks and Neural Language Models
11-12	Deep Learning Architectures for Sequence Processing
13-14-15	Sequence labelling POS tagging, NER, Tokenization
16-17-18	Parsing
19-20-21	Machine Translation
22-23-24	Semantics
25-26	Reference resolution, Discourse (Entity Linking)
27	Information/Relation Extraction
28-29	Question Answering
30-31	Summarization
32	Dialogue Systems
33-34	Sentiment Analysis
35	Ethics
36	Differential Privacy and NLP