UTCS Colloquium/AI: Lenhart K. Schubert/University of Rochester: "Towards Generic Knowledge Acquisition from Text" TAY 3.128, Thursday, March 26, 2009 10:00 a.m.

Contact Name: 
Jenna Whitney
Date: 
Mar 26, 2009 10:00am - 11:00am

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http://www

.cs.utexas.edu/department/webevent/utcs/events/cgi/eidshow.cgi?person=Lenha

rtK.Schubert

Type of Talk:  UTCS Colloquium/AI

Speaker/Af

filiation:  Lenhart K. Schubert/University of Rochester 

Date/Time:  Thursday, March 26, 2009 10:00 a.m.

Location:&nbs

p; TAY 3.128

Host:  Vladimir Lifschitz

Talk Title:&nbsp

; "Towards Generic Knowledge Acquisition from Text"

Talk
Abstract:
Knowledge extraction from text has become an increasingly a

ttractive way to tackle the long-standing "knowledge acquisition bott

leneck" in AI, thanks to the burgeoning of on-line textual materials

, and continuing improvements in natural language processing tools. In the
KNEXT project (started 8 years ago at U. Rochester) we have been using com

positional interpretation of parsed text to derive millions of general &quo

t;factoids" about the world. Some examples, as translated from logi

cal encodings into approximate English by KNEXT, are: CLOTHES CAN BE WASHE

D; PERSONS MAY SLEEP; A CHARGE OF CONSPIRACY MAY BE PROVEN IN SOME WAY;

A MOUSE MAY HAVE A TAIL; A CAT MAY CATCH A MOUSE; etc. Viewed conservativ

ely as existential or possibilistic statements, such factoids unfortunatel

y do not provide a strong basis for reasoning. We would be better off with

broadly quantified claims, such as that ANY GIVEN MOUSE ALMOST CERTAINLY H

AS A TAIL, and IF A CAT CATCHES A MOUSE, IT WILL USUALLY KILL IT. How can
we obtain such stronger knowledge? I will discuss several approaches that

we are currently developing. Some involve further abstraction from KNEXT fa

ctoids using lexical semantic knowledge, while others involve direct inter

pretation of general facts stated in English. In all cases, issues in the

formal representation of generic knowledge are encountered, of the type mu

ch-studied in linguistic semantics under such headings as "donkey ana

phora", "dynamic semantics", and "generic passage

s". I will suggest a Skolemization approach which can be viewed as a

method of generating frame-like or script-like knowledge directly from lang

uage.

Speaker Bio:
Lenhart Schubert is a professor of Compu

ter Science at the University of Rochester, with primary interests in natu

ral language understanding, knowledge representation and acquisition, rea

soning, and self-awareness. While earning a PhD in Aerospace Studies at th

e University of Toronto, he became fascinated with AI and eventually joine

d the University of Alberta Computing Science Department and later (in 1988

), the University of Rochester. He has over 100 publications in natural la

nguage processing and semantics, knowledge representation, reasoning, an

d knowledge acquisition, has chaired conference programs in these areas,

and is a AAAI fellow and former Alexander von Humboldt fellow.