CS 6501-007 Natural Language Processing

1. Highlights

We will use Zoom for our online teaching. Students can find the Zoom links on Collab, under the Online Meetings tab. We will also record our lectures and upload to Collab.

About final project

2. Course Description

Natural language processing (NLP) seeks to provide computers with the ability to process and understand human language intelligently. Examples of NLP techniques include (i) automatically translating from one natural language to another, (ii) analyzing documents to answer related questions or make related predictions, and (iii) generating texts to help story writing or build conversational agents. This course, consisting of one fundamental part and one advanced part, will give an overview of modern NLP techniques.

2.1 Topics

This course will mainly focus on applying machine learning (particularly, deep learning) techniques to natural language processing. NLP topics covered by this course

  1. Text classification
  2. Language modeling
  3. Word embeddings
  4. Sequence labeling
  5. Machine translation and sequence-to-sequence models
  6. Some advanced topics: large-scale pre-trained language modelsing (e.g. BERT), generative models, natural language generation, interpretability in NLP

For detail information, please refer to the course schedule.

2.2 Prerequisites

2.3 Textbooks

Supplemental materials

3. Assignments and Final Project

4. Additional Information

Last updated on Jan. 27, 2021