CS 613: Natural Language Processing

IIT Gandhinagar

Autumn 2021


Instructor: Mayank Singh (email: singh.mayank@iitgn.ac.in)

Online Office Hours: Monday - 16:00 - 17:00

Class Schedule: Tuesday and Friday: 15:35 - 17:00

Location: Online (Zoom link will be shared over the email)

Communication Google group: cs613-2021.pvtgroup@iitgn.ac.in


TAs

Prerequisite (Optional)

  • Basic Probability & Statistics (ES 331/ MA 202) or equivalent

  • Basic understanding of Python programming (ES 102/ ES 112) or equivalent

Course Contents

  1. Text processing: Tokenization, Stemming, Spell Correction, etc.

  2. Language Modelling: N-grams, smoothing

  3. Morphology, Parts of Speech Tagging

  4. Syntax: PCFGs, Dependency Parsing

  5. Distributional Semantics, Topic Models

  6. Lexical Semantics, Word Sense Disambiguation

  7. Information Extraction: Relation Extraction, Event Extraction

  8. Applications: Text Classification, Sentiment Analysis, Opinion Mining, Summarization

  9. Deep Learning for NLP, Representation Learning

Practical Sessions (Optional, only for interested students)

  • Basics of Python for NLP (file handling, case-folding, spell check, split, strip, Regex, find, replace, etc.), NLTK, Anaconda installation, python notebooks, basic Github knowledge (pull, push, fork, merge, etc.). [Colab]

  • Basics of ML, such as Regression, classification. Test/train/validation, Cross-Validation(Why and How?). Using Numpy and Scikit-learn to train linear regression, SVM/Logistic/Random forest/decision trees for NLP tasks.

Additional sessions can be conducted based on request.

Projects

  • Will be updated soon.

Lecture Slides and Additional Materials


Assignments (All deadlines are 11:59PM IST)

  • Assignment I: Crawling data (Deadline: 16th August) [Link]

  • Assignment II: Processing and Understanding data (Deadline: 30th August) [Link]

  • Assignment III: Language Modelling (Deadline: 13th September) [Link]

Grading Policy & Schedule

  • Assignments (15%)
    Three assignments (    each carrying 5 marks).

  • Surprise quizzes (10%)
    Four quizzes of 2.5% marks each. These quizzes will assess your grasp of the content covered in the class.

  • Mid-semester (15%) [A Sample Paper]

  • End-semester (15%)

  • Attendance (5%)
    Ten surprise attendances of     0.5% marks each during (lectures and guest lectures).

  • Project (40%):

    • Attendance to each weekly meeting: 5%

    • Project proposal abstract: 5% (Template, Deadline: 23rd August)

    • Phase-I presentation: 5% (22nd September)

    • Phase-II presentation: 5% (28th October)

    • Final Project 3-minute madness video + Poster: 15% ( 5% +10%, 20th November) [See the poster and slides of 2019 NLP course version here]

    • Final Project Demo: 5% (22th November, Web platform)

Books