CSCE 689: Natural Language Processing Foundation and Techniques (Fall 2016)

Instructor: Ruihong Huang

  • Location: 104 HRBB
  • Time: TR 5:30-6:55 pm
  • Instructor Email:
  • Instructor Office: 402B HRBB
  • Credits: 3
  • Office Hours: Tue 11 am -12 pm or by appointment

  • [08/18] The first meeting will be on 08/30!

Course Description

This is an especially exciting time to study Natural Language Processing (NLP), which aims to enable computers to understand and automatically process human language. This course will focus on NLP fundamentals including language models, automatic syntactic processing and automatic semantic processing, discourse and pragmatics. In addition, this course will also introduce various applications of NLP, including information extraction, sentiment analysis, question and answering, text summarization and machine translation. The students will digest and practice their NLP knowledge and skills by working on programming assignments, in-class quizzes and a final project.

Course Goal

Through this course, students will gain solid theoretical knowledge and enough practical experience to design and develop their own text processing applications in the future.

Evaluation Metrics

You should expect for a lot of programming (four of them), frequent in-class quizzes (5 in total, roughly one after each two meetings), and a final project. In addition, you will be aawarded for active class participation, penalized for little participation. The good news is there's no final exam for this class!

Four Programming Assignments: 40%
Five in-class quizzes: 20%
Class participation: 10%
The Final Project: 30%

The grading policy is as follows:
90-100: A
80-89: B
70-79: C
60-69: D
<60: F

Attendance and Make-up Policies

Every student should attend the class, unless you have an accepted excuse. Please check student rule 7 for details.


It's important that you work on a real nlp project so that you earn first hand experience of basic text processing and learn to deal with high complexity of human language in concrete applications. You are responsible to develop your project ideas. Then the instructor is available to discuss and shape the project if you like. The scale of the project should be a semester long. By the end of the semester, you should submit your code and data for this project, write a project report of 8 pages at maximum, and prepare a class presentation.


Students should have taken the course Data Structure and Algorithms (CSCE 221).

Textbook and Material

Required textbook: Speech and Language Processing, Daniel Jurafsky and James H. Martin, 2008. Prentice Hall; 2nd edition. Relevant tutorials and papers will also be handed out during the class.

Academic Integrity

"An Aggie does not lie, cheat, or steal or tolerate those who do." For additional information, please visit:

Upon accepting admission to Texas A&M University, a student immediately assumes a commitment to uphold the Honor Code, to accept responsibility for learning, and to follow the philosophy and rules of the Honor System. Students will be required to state their commitment on examinations, research papers, and other academic work. Ignorance of the rules does not exclude any member of the TAMU community from the requirements or the processes of the Honor System.

Americans with Disabilities Act (ADA) Statement

The Americans with Disabilities Act (ADA) is a federal anti-discrimination statute that provides comprehensive civil rights protection for persons with disabilities. Among other things, this legislation requires that all students with disabilities be guaranteed a learning environment that provides for reasonable accommodation of their disabilities. If you believe you have a disability requiring an accommodation, please contact Disability Services, currently located in the Disability Services building at the Student Services at White Creek complex on west campus or call 979-845-1637. For additional information, visit

Tentative schedule

Week Date Topic Material Notes
1 08/30 Course Overview slides
09/01 Text Preprocessing and Regular Expressions slides p1 out
2 09/06 Text Classification and Naive Bayes slides
09/08 MaxEnt slides p1 due, p2 out
3 09/13 Language Modeling slides, quiz1 Sentence-level LM Discourse Driven LM
09/15 Smoothing slides,
4 09/20 Intro to Parts-of-Speech Tagging slides
09/22 Sequence Models slides HMM, CRF p2 due
5 09/27 more Sequence Models slides
09/29 Intro to Parsing slides quiz2
6 10/04 Statistical Parsing slides lexicalized PCFGs p3 out
10/06 Dependency Parsing slides
7 10/11 Semantics Intro slides quiz3
10/13 Vector Semantics slides
8 10/18 Semantic Role Labeling slides
10/20 intro to IE & sentiment lexicon induction slides p3 due, quiz4
9 10/25 Relation Extraction slides p4 out
10/27 Discourse, Pragmatics, Coreference Resolution slides
10 11/01 Event Extraction slides
11/03 no class, out of town
11 11/08 Sentiment Analysis slides quiz5
11/10 Question Answering slides p4 due
12 11/15 Text Summarization slides
11/17 Machine Translation slides
13 11/22 Deep Learning Invited Speaker Dr. Choe slides deep learning for NLP
11/24 Thanksgiving Holiday
14 11/29 Final Project Presentations
12/01 Final Project Presentations
15 12/06 Final Project Presentations
12/08 Reading day, no class