Natural Language Processing

Instructor: Dr. Chandresh Kumar Maurya

Lecture : Online (over Google meet, see calendar invite)

Time : Class on Sat from 3-5 pm, Tutorial on Thu 3-4pm. Thu 9am will be reserved

Office : 415 in POD A building

Meeting hours: 16:00-17:00 PM

**TA : M Ratna **

Text Books:

1. Daniel Jurafsky & James H. Martin : *Speech and Language Processing* : Pearson Education India : India : 2013 : 9789332518414  (2nd and 3rd ed.)
2. Manning and Schutze : *"Statistical Natural Language Processing* : MIT Press : Cambridge, MA : 1999 : 0262133601

What is this course about?

Natural language processing (NLP) or computational linguistics is one of the most important technologies of the information age. Applications of NLP are everywhere because people communicate almost everything in language: web search, advertising, emails, customer service, language translation, virtual agents, medical reports, etc. In the last decade, deep learning (or neural network) approaches have obtained very high performance across many different NLP tasks, using single end-to-end neural models that do not require traditional, task-specific feature engineering. In this course, students will gain a thorough introduction to cutting-edge research in Deep Learning/machine learning for NLP. Through lectures, assignments and a final project, students will learn the necessary skills to design, implement, and understand basics of NLP.

Prerequisites:

1. Proficiency in Python
2. College Calculus, Linear Algebra
3. Basic Probability and Statistics
4. Foundations of Machine Learning

Grading policy:

1. Assignments– There will be weekly assignments.
2. Projects– One Major that will lead to a short paper
3. MSE
4. ESE

Projects:

• Pick a paper from ACL 2021 proceedings link.
• Submit a proposal in Stanford overleaf template ( link ) following the guidelines given in the template by Sat, Jan 15. You can form a team of at most 3 people (5 marks).
• At the end of the course, you will submit your paper in the ACL format link.

Assignments

1. See LMS for assignments

Lecture Slides (Videos are only accessible inside IIT Indore)

Tutorials