Natural Language Processing

Announcements !!

• Attendance will be handled by TAs Amogh and Rohit. All communication regarding attendance, if any, should be directed to these TAs only.  

• First class on Monday, Jan 5, 2026

• NLP is a very popular course, and there are many more requests for registration than what can be accommodated with the available resources. Hence, registration requests will be approved on ERP gradually in batches, taking into consideration the CGPA and seniority of the students. Please do not send emails to the faculty member / TAs regarding registration to the course; we regret to say that individual emails about registration cannot be answered.

• This course will require students to understand several CS research papers. There will be assignments that will involve substantial programming in Python. It is advisable to take this course only if you have the necessary background (see below).

Broad topics

• Challenges in NLP

• Parts of Speech Tagging

• Syntax, Dependency Parsing

• Language Modeling: N-grams, smoothing

• Distributional Semantics, Word Embeddings

• RNNs and seq2seq models

• Transformers, Attention, BERT

• Large Language Models, Prompt Engineering

• Applications and special topics (domain-specific NLP, interpretability, etc.)

Text and Reference Literature

• Daniel Jurafsky and James Martin. Speech and Language Processing. https://web.stanford.edu/~jurafsky/slp3/

• Christopher Manning and Hinrich Schütze. Foundations of Statistical Natural Language Processing. https://nlp.stanford.edu/fsnlp/

• Goldberg. A Primer on Neural Network Models for Natural Language Processing. https://arxiv.org/abs/1510.00726

• Research papers and online materials to be pointed out in class, especially for the recent topics

Plagiarism policy

• Plagiarism in any form - copying from other students or from online resources - will be severely penalized. Every assignment should be done individually or in designated groups. Also you should not use / copy any code that is available online.

• While you can discuss the concepts and assignments with other students/groups, you should NOT share your code/answers for any assignment with any other student (or with students outside your designated group), until the grading of the assignment is completed. It is your responsibility to ensure that your codes/answers are not available to others.

• We will use standard plagiarism detection software to check the similarity of submitted assignments. If we find submissions that are too similar (beyond what can be expected by chance, or due to discussion), all such submissions will be severely penalized. We will NOT attempt to differentiate between who gave the codes and who copied; all involved students will be penalized equally. The minimum penalty for plagiarism in an assignment is a zero on that assignment. There can be more severe penalties for repeat offences, including grade reduction and de-registration from the course.