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GRANULAR CAUSALITY FOR LEARNING BY READING
by
Rutu Mulkar-Mehta
A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(COMPUTER SCIENCE)
December 2011
Copyright 2011 Rutu Mulkar-Mehta
Object Description
| Title | Granular causality for learning by reading |
| Author | Mulkar-Mehta, Rutu |
| Author email | me@rutumulkar.com;me@rutumulkar.com |
| Degree | Doctor of Philosophy |
| Document type | Dissertation |
| Degree program | Computer Science |
| School | Viterbi School of Engineering |
| Date defended/completed | 2011-08-26 |
| Date submitted | 2011-10-26 |
| Date approved | 2011-10-26 |
| Restricted until | 2011-10-26 |
| Date published | 2011-10-26 |
| Advisor (committee chair) | Hobbs, Jerry R. |
| Advisor (committee member) |
Hovy, Eduard Gordon, Andrew Arbib, Michael A. Kaiser, Elsi |
| Abstract | It has long been the vision of AI researchers to build systems that are able to learn and understand causal patterns in discourse as they read input text, so that new inferences can be made on the input discourse, and numerous causal patterns can be extracted from texts that may be from relatively different domains. Discovering causal relations has proved to be a challenging research problem. One reason for this is that causal markers are dependent on the domains and genres of English discourse that they exist in. For instance, causal markers in football articles, are different from causal markers in bio-medical articles. In this thesis I prove the domain and genre dependence of causal markers. Most previous work for discovering causality has focused on either limited domains, or the top most frequent patterns representing causality in language. Both of these approaches have the shortcomings of being able to extract only a small set of causal relations. To have a wider coverage, there is an urgent need to develop systems that are able to discover domain dependent causal markers as well as low frequency causal markers in text. This thesis aims at solving this problem. ❧ I introduce a Theory of Granular Causality as it exists in natural language discourse. Although the phenomenon of granular causality is very common in discourse, there has been very limited work for understanding it, and no work done to extract such relations from discourse. In this thesis, I propose a Theory of Granular Causality, and prove the features of this theory using a human annotation study. Next, elaborate on how this theory can be used to discover causal markers from small domains of discourse, addressing the problems raised by the domain dependent nature of causal markers. Additionally, I extend this theory to discover causal relations from discourse which are not marked by a causal marker, i.e., causality between two sentences. Finally, I apply this theory for answering ”how” style causal questions, which are the second most popular questions on the web after factoid (when, when) questions. ❧ This dissertation is the first look at granular causality as a phenomenon in natural language, and applications of this theory for solving challenging problems. |
| Keyword | natural language processing; artificial intelligence; learning by reading; causality; granularity; information extraction |
| Language | English |
| Part of collection | University of Southern California dissertations and theses |
| Publisher (of the original version) | University of Southern California |
| Place of publication (of the original version) | Los Angeles, California |
| Publisher (of the digital version) | University of Southern California. Libraries |
| Provenance | Electronically uploaded by the author |
| Type | texts |
| Legacy record ID | usctheses-m |
| Contributing entity | University of Southern California |
| Rights | Mulkar-Mehta, Rutu |
| Physical access | The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the author, as the original true and official version of the work, but does not grant the reader permission to use the work if the desired use is covered by copyright. It is the author, as rights holder, who must provide use permission if such use is covered by copyright. The original signature page accompanying the original submission of the work to the USC Libraries is retained by the USC Libraries and a copy of it may be obtained by authorized requesters contacting the repository e-mail address given. |
| Repository name | University of Southern California Digital Library |
| Repository address | USC Digital Library, University of Southern California, University Park Campus MC 7002, 106 University Village, Los Angeles, California 90089-7002, USA |
| Repository email | cisadmin@dots.usc.edu |
| Archival file | uscthesesreloadpub_Volume71/etd-MulkarMeht-360.pdf |
Description
| Title | Page 1 |
| Contributing entity | University of Southern California |
| Repository email | cisadmin@dots.usc.edu |
| Full text | GRANULAR CAUSALITY FOR LEARNING BY READING by Rutu Mulkar-Mehta A Dissertation Presented to the FACULTY OF THE USC GRADUATE SCHOOL UNIVERSITY OF SOUTHERN CALIFORNIA In Partial Fulfillment of the Requirements for the Degree DOCTOR OF PHILOSOPHY (COMPUTER SCIENCE) December 2011 Copyright 2011 Rutu Mulkar-Mehta |
