Speech and Natural Language Processing - CS60057

Fall Semester - 2014-15

Instructor

Pawan Goyal

Course Timings

Lectures

Monday - 16:30 - 17:30 (NR-222)

Thursday - 16:30 - 17:30 (NR-222)

Friday - 16:30 - 17:30 ((NR-222)

Reserved Slot: Friday - 7:30 - 8:25 ((NR-222)

Office Hours: Friday - 5:30 - 7:00 PM (CSE - 308)

Teaching Assistants

Abhishek Gupta, Dewesh Agrawal

SNLP Projects

Results are out, congratulations to all the winners

Winner: Word Labeling and Mixed-script Ad-hoc retrieval for Hindi song lyrics, Siddharth Rakesh and Team

3 Runner-ups: TDLR News: A summarization tool for news articles (Utkarsh Jaiswal and Team), Understanding shifts in trends/topics in the scientific domain (Mayank Singh and Team), Automatic Highlight Generation of Cricket matched and Sentiment Analysis of Corresponding Tweets in real-time (Vikas Sahu and Team)

Announcements

End-sem exam on November 28th, 2:00 - 5:00 PM

Next Class on September 25th, then from October 9th.

Mid-semester examination on September 23rd, 2:00 - 4:00 PM. Syllabus till September 12th.

Mid-sem report of the term project to be submitted by September 9th, 10:30 PM, details available on moodle.

Classroom changed from CSE-119 to NR222. From July 24th, classes will be in NR222 (Nalanda Complex).

The Lecture-Slides have been uploaded on Moodle.

The course will start from July 17th.

Reference Books

  1. Daniel Jurafsky and James H. Martin. 2009. Speech and Language Processing: An Introduction to Natural Language Processing, Speech Recognition, and Computational Linguistics. 2nd edition. Prentice-Hall.
  2. Christopher D. Manning and Hinrich Schütze. 1999. Foundations of Statistical Natural Language Processing. MIT Press.

Course Contents

Major Components of the Course include:
  1. Basic Text Processing: Tokenization, Stemming
  2. Language Modeling: N-grams, smoothing
  3. Morphology, Parts of Speech Tagging
  4. Syntax: PCFGs, Dependency Parsing
  5. Distributional Semantics
  6. Lexical Semantics, Word Sense Disambiguation
  7. Information Extraction: Relation extraction, Event extraction
  8. Text Summarization
  9. Text Classification
  10. Machine Translation