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.
There were will be 4 problem sets during the class, due roughly every
three weeks. The problem sets will include both theoretical problems and
There will be a midterm and a final in the class.
The midterm will be in class in mid October.
The overall grade will be determined roughly as follows:
Midterm 25%, Final 40%, Problem sets 35%.
Here is a tentative syllabus for class:
- 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
There are comprehensive notes for the class, posted