acl acl2013 acl2013-387 acl2013-387-reference knowledge-graph by maker-knowledge-mining
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Author: Jong-Hoon Oh ; Kentaro Torisawa ; Chikara Hashimoto ; Motoki Sano ; Stijn De Saeger ; Kiyonori Ohtake
Abstract: In this paper, we explore the utility of intra- and inter-sentential causal relations between terms or clauses as evidence for answering why-questions. To the best of our knowledge, this is the first work that uses both intra- and inter-sentential causal relations for why-QA. We also propose a method for assessing the appropriateness of causal relations as answers to a given question using the semantic orientation of excitation proposed by Hashimoto et al. (2012). By applying these ideas to Japanese why-QA, we improved precision by 4.4% against all the questions in our test set over the current state-of-theart system for Japanese why-QA. In addi- tion, unlike the state-of-the-art system, our system could achieve very high precision (83.2%) for 25% of all the questions in the test set by restricting its output to the confident answers only.
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