CSCE 638: Natural Language Processing Foundation and Techniques (Fall 2018)

Instructor: Ruihong Huang

  • Location: HRBB 113
  • Time: TR 12:45-2:00 pm
  • TA: Wenlin Yao
  • Instructor Email:
  • Instructor Office: 402B HRBB
  • TA Email:
  • TA Office: 402A HRBB
  • Credits: 3
  • Office Hours: Tue 9:30 am - 11:30 am or by appointment
  • TA Office Hours: Thr 3:00 pm - 5:00 pm or by appointment

  • [08/28] The first meeting will be on 08/28!

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), an annotation assignment, a final project and a final exam. In addition, you will be awarded for active class participation, penalized for little participation.

Four Programming Assignments: 40%
Annotation Assignment: 10%
The Final Project: 25% (abstract: 5%, presentation+report+code+data: 20%)
Class participation: 5%
Final Exam (Dec. 12th, 8:00-10:00 am): 20%

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

Date Topic Material Notes
08/28 Course Overview slides
08/30 Text Preprocessing and Regular Expressions slides p1 out
09/04 Text Classification slides
09/06 Naive Bayes slides
09/11 Discriminative Models: MaxEnt, Perceptron slides p1 due, p2 out
Language Modeling
09/13 Language Modeling slides Sentence-level LM Discourse Driven LM
09/18 Smoothing slides
09/20 Intro to Parts-of-Speech Tagging slides
09/25 Sequence Models slides HMM, CRF p2 due
09/27 Sequence Models cont. slides
10/02 Intro to Parsing slides project abstract due
10/04 Statistical Parsing slides annotation assignment out
10/09 Statistical Parsing cont. slides lexicalized PCFGs
10/11 Intro to Dependency Parsing slides
10/16 mid-term review annotation assignment due, p3 out
10/18 Intro to Semantics slides
10/23 Thesaurus-based Word Similarity slides
10/25 Distributional Semantics slides word vectors
10/30 Dense Vectors slides p3 due, word2vec
11/01 Semantic Role Labeling slides SRL
Information Extraction
11/06 Intro to IE slides p4 out
11/08 Relation Extraction slides
11/13 Coreference Resolution slides
11/15 Event Extraction slides p4 due
Deep Learning
11/20 Deep Learning slides deep learning for NLP
11/22 no class Thanksgiving holiday! project due
11/27 Final Project Presentations slides
11/29 Final Project Presentations slides
12/04 Final Project Presentations slides
12/06 Reading day, no class
12/12 (Wed) Final Exam, 8:00-10:00 am