An unsupervised approach to identifying causal relations from relevant scenarios
Riaz, Mehwish
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https://hdl.handle.net/2142/14759
Description
Title
An unsupervised approach to identifying causal relations from relevant scenarios
Author(s)
Riaz, Mehwish
Issue Date
2010-01-06T17:50:06Z
Director of Research (if dissertation) or Advisor (if thesis)
Girju, Roxana
Doctoral Committee Chair(s)
Girju, Roxana
Department of Study
Computer Science
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
Causality
Semantic Relations
Topics
Unsupervised Learning
Abstract
Semantic relations between various text units play an important role in natural language
understanding, as key elements of text coherence. The automatic identification of these
semantic relationships is very important for many language processing applications. One
of the most pervasive yet very challenging semantic relations is cause-effect. In this
thesis, an unsupervised approach to learning both direct and indirect cause-effect
relationships between inter- and intra-sentential events in web news articles is proposed.
Causal relationships are leaned and tested on two large text datasets collected by crawling
the web: one on the Hurricane Katrina, and one on Iraq War. The text collections thus
obtained are further automatically split into clusters of connected events using advanced
topic models. Our hypothesis is that events contributing to one particular scenario tend to
be strongly correlated, and thus make good candidates for the causal information
identification task. Such relationships are identified by generating appropriate candidate
event pairs. Moreover, this system identifies both the Cause and Effect roles in a
relationship using a novel metric, the Effect-Control-ratio. In order to evaluate the
system, we relied on the manipulation theory of causality
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