HG2051 – Language and the Computer (AY2024-25)

Hiram Ring <hiram.ring@ntu.edu.sg>

Thursdays, 9:30am-12:20pm (Sem1)

TR+29 (LHS-B2-06, The Hive)

Traditionally linguistic analysis was done largely by hand, but computer-based methods and tools are becoming increasingly widely used in contemporary research. This course provides an introduction to skills and resources to assist the linguist in performing fast, flexible, and accurate quantitative analyses. Students will learn a programming language (Python) along with techniques for processing human language data. No previous programming experience is required: we will teach you the basics of programming and computational linguistics along with some good software engineering practices.

Schedule

Week Date Topic Notes
1 15 Aug What is Computational Linguistics? Why do it? Why use Python?
Computer Science basics
Setup, VS_Code
2 22 Aug Basic Types and Data Structures; Using Python to Count Things; Lists PyT 3.1; NLTK 1
3 29 Aug Assignment, Expressions, and Control; Strings PyT 3.2, 4; NLTK 4.1
4 5 Sep Text Corpora and Conditional Frequencies DIP 2.2, 2.8; PT 5.3, 5.5, 4.7.1-2; NLTK 2.1-2; lecture, practice
5 12 Sep Lexical Resources and WordNet NLTK 2.4, 2.5, (How To); lecture; practice
6 19 Sep Processing Raw Text NLTK 3.1, 3.3; PT 7.1-7.3; lecture; practice
7 26 Sep Mid-review; Working with Software Projects Coding challenge; PT 6, 6.4
03 Oct Recess
8 10 Oct Algorithmic Thinking and Regular Expressions NLTK 3.4, 5, 6, 7, 8; lecture; practice
9 17 Oct N-Grams and Collocations NLTK 4.5, 5; lecture; practice
10 24 Oct Part-of-speech Tagging NLTK 5.4, 5, 7; practice
31 Oct Deepavali
11 07 Nov Classification NLTK 6.2, 5, 6, What is AI?; Project 1 due, 11:59pm; practice, enamdict
12 14 Nov Ethics, Language Models, and Software Libraries
12 15 Nov Alternate Final Quiz date (9:30am TR+29) Coding challenge
13 21 Nov Review and Final Quiz Coding challenge
28 Nov Project 2 due, 11:59pm

Course Pages

Grading Criteria

This course is graded with continuous assessment as follows:

You may also get 1–5% extra credit (not exceeding 100% in the course) by submitting a contribution (e.g., code or documentation) to an open-source project. Contact me for details.

Resources

Acknowledgments

The majority of the content for this course has been borrowed (with permission) from Michael Wayne Goodman and Francis Bond, who taught previous years. Below are some of the archives of the previous courses: