Class Time and Location:
Monday/Wednesday 10:25 - 11:40, Mel. 269
Course Description:
Instructor:
Prerequisites: No formal prerequisites, but programming experience
and mathematical maturity are presupposed.
This module is an introduction to natural language processing, with
emphasis on computational techniques for deriving the structure and
meaning of natural language.
Topics include English phrase structure, dependency structure, parsing with
traditional algorithms and with neural nets,
representing meaning and knowledge, and a brief introduction to
inference.
Len Schubert
Department of Computer Science
Room 3003, Wegmans Hall
University of Rochester
E-mail:
lastname at cs dot rochester dot edu
Office Hours: Tuesday and Thursday 5:00 - 6:30 (or by appointment)
Some Broad-Coverage Sources for NLU:
Academic honesty policy:
In your problem-solving and programming, you are expected to use your own ideas, your own words, and your own code, unless otherwise indicated (e.g., you are advised to use some existing code). Where you do use someone else's solutions or code (not prescribed by the instructor), explicitly cite your source.
Assignments:
Assignment 1
Assignment 2
Supplementary Course Materials:
Some course materials may be made available through this web page.
Meaning, significance, and methods of NLPLong Short-Term Memory (LSTM) NNs for Natural Language Processing
Peephole LSTM (from Wikipedia)
NN-based inorder parsing example.pdf
Transition-based parser architecture
Shift-project-reduce actions for inorder parsing
Inorder parsing in symbolic form
James Allen's basic parser is located
here
(or look for it on
James Allen's web page).
Slides by James Allen
Overview of NLU, Syntax, and Chart ParsingAdditional lecture slides:
01: basic linguistic concepts
