An unsupervised approach to identifying causal relations from relevant scenarios

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

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2010

Author dc:creator

• Riaz, Mehwish

Contributors dc:contributor

• Girju, Roxana

• Causality
• Semantic Relations
• Topics
• Unsupervised Learning

Statement dc:rights

• Copyright 2009 Mehwish Riaz

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/14759

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/14759