Speaker/Affiliation: Dan Ju

rafsky/Stanford University

Date/Time: Friday November 16 2007 11

:00 a.m.

Location: ACES 2.402

Host: Ray Mooney

Talk T

itle: Inducing Semantic Taxonomies for Words

Talk Abstract:
Onli

ne resources for word meaning like dictionaries and
thesauri are a usef

ul resource for natural language processing.
But English has a lot of w

ords and adds more every day.
Hand-built resources can''t keep up. We

report on three studies
on inducing the meaning of words from text on t

he Web in
the context of augmenting WordNet a large online thesaurus <

br>of English. We first describe a semi-supervised method for
learning

when a new word is a %60hypernym'' or in the ''is-a''
relation with ano

ther word based on combining %60weak learner''
hypernym detection patt

erns. We then show a new probabilistic
algorithm for taxonomy induction
which incorporates evidence
from multiple relation detectors (for hypo

nymy and synonymy)
to optimize the entire structure of the taxonomy. Fi

nally
we show how to improve the granularity of word senses in
a th

esaurus by clustering word senses. This talk describes
joint work with

Rion Snow and Andrew Ng.

Speaker Bio:
Dan Jurafsky is an associat

e professor in the Department
of Linguistics and by courtesy in the De

partment of Computer
Science at Stanford University. Before Stanford h

e taught
for 8 years at the University of Colorado Boulder. Dan
st

udies statistical models of human and machine processing
of text and sp

eech focusing lately on computational models
of semantics and discours