**Instructor:**
Michael Collins

## Course Description:

COMS W4705 is a graduate introduction to natural language processing, the
study of human language from a computational perspective. We will
cover syntactic, semantic and discourse processing models. The
emphasis will be on machine learning or corpus-based methods and
algorithms. We will describe the use of these methods and models in
applications including syntactic parsing, information extraction,
statistical machine translation, dialogue systems, and summarization.

## Problem sets:

There were will be 4 problem sets during the class, due roughly every
three weeks. The problem sets will include both theoretical problems and
programming assignments.
## Exams:

There will be a midterm and a final in the class.
The midterm will be in class in mid October.
## Grading:

The overall grade will be determined roughly as follows:
Midterm 25%, Final 40%, Problem sets 35%.
## Syllabus:

Here is a tentative syllabus for class:

- Introduction
- Estimation techniques, and language modeling
- Tagging, hidden Markov models
- Statistical parsing
- Log-linear models
- Feedforward networks for NLP
- Word embeddings
- Further topics on neural networks in NLP

## Readings:

There are comprehensive notes for the class, posted
here.