Linguistics 3102B
Linguistics Analysis with Computers

Prerequisite(s):  Linguistics 2248A/B or French 3810A/B or Spanish 3317A/B, or permission of the Department of French Studies program.

It is the student’s responsibility to check the course prerequisites and antirequisites.
Unless you have either the requisites for this course or written special permission from the Department of French Studies to enroll in it, you may be removed from this course and it will be deleted from your record. This decision may not be appealed. You will receive no adjustment to your fees in the event that you are dropped from a course for failing to have the necessary prerequisites.

Course Description:
This course allows students to better understand language analysis and theory by providing a set of basic programming tools that they will use to hypothesize and analyze linguistics data, to manipulate and modify corpuses and eventually create specific kinds of data structure. Specific problems of morphology, syntax, corpus analysis, and other questions related to Natural Language Processing, will be addressed.

Students who take this course are expected to have a basic knowledge of syntax and linguistics, but do not need programming experience. Students should be aware that succeeding in this course requires ATTENTION TO DETAILS and a willingness to scrupulously follow INSTRUCTIONS given in class, especially if they have no prior experience with computer programming. 

Pedagogical Objectives:
Upon successful completion of this course, students will be able to:

  • Use and modify simple programs in the programming language Python.
  • Download different corpus, learn how to modify them, tag them and save them for specific tasks
  • Tests predictions of specific analyses provided by the professor over corpus
  • Create own language data in standard formats and use it to evaluate the performance of NLP programs.

 

Course Materials:

  • Course website: the content of the course will be presented through weekly lessons available on the OWL course website
  • The textbook used by instructor: Natural Language Processing with Python -Analyzing Text with the Natural Language Toolkit by Steven Bird, Ewan Klein, and Edward Loper (version 2019 freely available at http://www.nltk.org/book/)

 
Methods of Evaluation (subject to change):
Bi-weekly progress check: 30 %
Two assignments at 15% each: 30 %
Final project: plan and final project
Plan for the project: 10 points 10%
Final project: 30 points 30%
Total: 100%