2026-09-18T20:15:29Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/14759 2023-07-10 col_2142_10761 col_2142_5131 com_2142_10755 com_2142_234 com_2142_5130

Girju, Roxana Girju, Roxana Riaz, Mehwish 2010-01-06T17:50:06Z 2010-01-06T17:50:06Z 2012-01-07T11:00:14Z 2010-01-06T17:50:06Z 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 In order to evaluate the system, we relied on the manipulation theory of causality Item withdrawn by Mark Zulauf (zulauf@illinois.edu) on 2009-11-17T14:25:54Z Item was in collections: University of Illinois Theses & Dissertations (ID: 1) No. of bitstreams: 1 Riaz_Mehwish.pdf: 328479 bytes, checksum: 0528bfcbeaebb7e842cb7d6ee2a87956 (MD5) Made available in DSpace on 2010-01-06T17:50:06Z (GMT). No. of bitstreams: 3 Riaz_Mehwish.pdf: 328479 bytes, checksum: 0528bfcbeaebb7e842cb7d6ee2a87956 (MD5) 1_Riaz_Mehwish.pdf: 328571 bytes, checksum: b2446a9b95ba630d9e93980789728c6b (MD5) license.txt: 4060 bytes, checksum: 5945829a1f898136ddda9a9f66304011 (MD5) Item marked as restricted to the 'Administrator' Group (id=1) by William Ingram (wingram2@illinois.edu) on 2010-01-06T17:50:31Z Item is restricted until 2012-01-06T17:50:31Z Item reinstated by Sarah Shreeves (sshreeve@illinois.edu) on 2012-01-07T11:00:14Z Item was in collections: Dissertations and Theses - Computer Science (ID: 587) University of Illinois Dissertations and Theses (ID: 204) No. of bitstreams: 4 1_Riaz_Mehwish.pdf.txt: 78365 bytes, checksum: 6fed6472a1f60fc1096532e65e8e33cd (MD5) Riaz_Mehwish.pdf: 328479 bytes, checksum: 0528bfcbeaebb7e842cb7d6ee2a87956 (MD5) 1_Riaz_Mehwish.pdf: 328571 bytes, checksum: b2446a9b95ba630d9e93980789728c6b (MD5) license.txt: 4060 bytes, checksum: 5945829a1f898136ddda9a9f66304011 (MD5) Item released from any restrictions by Sarah Shreeves (sshreeve@illinois.edu) on 2012-01-07T11:00:14Z http://hdl.handle.net/2142/14759 en Copyright 2009 Mehwish Riaz Causality Semantic Relations Topics Unsupervised Learning An unsupervised approach to identifying causal relations from relevant scenarios 2009-12 Computer Science 1434 Computer Science 0112 University of Illinois at Urbana-Champaign Thesis M.S. PHD:Computer Science -UIUC 10KS0112PHD