Credit Hours: 4
Class Time and Location:
Tuesday/Thursday 9:40 - 10:55, CSB 632
Course Description:
Instructor:
TA:
Prerequisites: CSC242
Course Goals and Schedule:
CL, Ann. Meet. of the ACL, EACL, COLING, ANLP, Linguistics and
Philosophy, special workshops (e.g., AAAI Spring and Fall Symposia,
workshops on speech, discourse, dialogue, large corpora,
knowledge extraction, question-answering, etc.)
General AI literature: AIJ, JAIR, CI, Cog. Sci., AAAI,
IJCAI, ECAI, ...
Textbook:
Supplementary Texts (on reserve in Carlson library):
Grading:
Homework:
The project/essay for graduate students can be on a freely chosen
topic in natural language processing, but could also be an elaboration,
in some direction, of the assigned project. (But in all cases some
delving into the relevant literature is expected; i.e., even
a programming-oriented project should describe how the work relates
to the state of the art, though in less depth than an essay-style
project.) Essays/project writeups are typically 3500-7000 words in length,
and should be well-written and well-organized, and should include
illustrative examples, relevant citations, and should show the author's
ability to digest and clearly present (in his or her own words)
technically nontrivial material and to contribute novel ideas and
a personal perspective. Project topics should be thought about as
early as possible. Some more detailed guidelines will be provided.
Academic honesty policy:
Students may discuss among themselves the nature of
the problems or tasks assigned, but not the solutions.
If you collaborated with someone (namely, worked on a problem
with someone or shared your solution with someone),
then on your paper report who the persons are.
The points you earn on that particular problem will be
divided by the total number of students involved in
the collaboration. Failure to report collaboration, if detected,
will result in a penalty---the entire score of the paper will
be reduced to zero.
This course is an introduction to natural language processing, with
emphasis on constructing computer programs that understand natural
language.
Topics include English phrase structure, parsing, semantic analysis,
speech acts, knowledge representation, and NL system design.
Len Schubert
Department of Computer Science
Room 733, Computer Studies Building
University of Rochester
E-mail:
lastname at cs dot rochester dot edu
Office Hours: Tuesday and Thursday 5:00 - 6:00 (or by appointment)
Francis Ferraro
(email: f followed without gap by ferraro at u dot etc.)
Frank's website
for CSC 247/447
Office Hours: TBA
Knowledge of LISP (or scheme) is assumed, and some acquaintance with
context-free grammars (e.g., from CSC 280, CSC 254, LIN 110 (or better
260or LIN 225)) is helpful.
A prior logically oriented course such as CSC 244, CSC 219, or PHL 110
is also very desirable.
The goals are to learn ...
In more general terms, after taking this course you should
The course
schedule made available through this link
(as a pdf file) will also be handed out in the first
class. The schedule is approximate and subject to adjustment.
James Allen,
Natural Language Understanding,
Benjamin/Cummings, 2nd edition, 1995.
The following contribute to the grades (where percentage weights
may be adjusted upward or downward by up to 3%):
The homework assignments will include problems requiring written
answers and Lisp programming. The problems will be based on the lectures
that have been given.
The later Lisp assignments may form a staged
NLP project, perhaps aimed at simple text summarization, or knowledge
extraction, or something similar. This may well involve use of some
pre-existing code, such as parsers and logical form generators. It
may also involve use (and perhaps further development) of resources
such as lexicons and text or treebank corpora.
In your writing, 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 code written by the TA, or from some other source).
Where you do use another author's text verbatim, give a proper
citation, and similarly, where you make permissible use of
code from a non-prescribed source, cite your source.
The following applies to assignments that you've been asked to
do individually (as opposed to any group project that might
possibly be assigned, and explicitly designated as such).
Supplementary Course Materials:
Some course materials may be made available through this (or the TA's) web page.
Lectures will involve use of overhead slides and the board, and copies of the slides (and some other informal notes) will be provided to students via the web or as hard copy.
Meaning, significance, and methods of NLP
Top-down parse example
James Allen's basic parser is located
here
(or look for it on
James Allen's web page).
Slides from lectures:
(A slide-printing script is available
here, and now a 10 times
faster version (with only slightly lower resolution) is available
here,
both thanks to Fabrizio Morbini.)
01: basic linguistic concepts07: Auxiliaries, Passives, Imperatives, Y-N-Questions
08: Questions, Gaps, and Parsing with Gaps
09: Other Unbounded Dependencies
10: Towards Deterministic Parsing
14: Semantic Interpretation Intro
17: Interpreting PPs, Simple Relatives, and Yes-No Questions