Learning cause identifiers from annotator rationales

Authors:
Muhammad Arshad Ul Abedin;Vincent Ng;Latifur Rahman Khan
Affiliations:
Department of Computer Science, University of Texas at Dallas, Richardson, TX;Department of Computer Science, University of Texas at Dallas, Richardson, TX;Department of Computer Science, University of Texas at Dallas, Richardson, TX
Venue:
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Year:
2011

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Cause identification from aviation safety incident reports via weakly supervised semantic lexicon construction

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Abstract

In the aviation safety research domain, cause identification refers to the task of identifying the possible causes responsible for the incident described in an aviation safety incident report. This task presents a number of challenges, including the scarcity of labeled data and the difficulties in finding the relevant portions of the text. We investigate the use of annotator rationales to overcome these challenges, proposing several new ways of utilizing rationales and showing that through judicious use of the rationales, it is possible to achieve significant improvement over a unigram SVM baseline.