Paper Info
Title
Mining Causality from Texts for Question Answering System
Abstract
This research aims to develop automatic knowledge mining of causality from texts for supporting an automatic question answering system (QA) in answering 'why' question, which is among the most crucial forms of questions. The out come of this research will assist people in diagnosing problems, such as in plant diseases, health, industrial and etc. While the previous works have extracted causality knowledge within only one or two adjacent EDUs (Elementary Discourse Units), this research focuses to mine causality knowledge existing within multiple EDUs which takes multiple causes and multiple effects in to consideration, where the adjacency between cause and effect is unnecessary. There are two main problems: how to identify the interesting causality events from documents, and how to identify the boundaries of the causative unit and the effective unit in term of the multiple EDUs. In addition, there are at least three main problems involved in boundaries identification: the implicit boundary delimiter, the nonadjacent cause-consequence, and the effect surrounded by causes. This research proposes using verb-pair rules learnt by comparing the Naïve Bayes classifier (NB) and Support Vector Machine (SVM) to identify causality EDUs in Thai agricultural and health news domains. The boundary identification problems are solved by utilizing verb-pair rules, Centering Theory and cue phrase set. The reason for emphasizing on using verbs to extract causality is that they explicitly make, in a certain way, the consequent events of cause-effect, e.g. 'Aphids suck the sap from rice leaves. Then leaves will shrink. Later, they will become yellow and dry.'. The outcome of the proposed methodology shown that the verb-pair rules extracted from NB outperform those extracted from SVM when the corpus contains high occurence of each verb, while the results from SVM is better than NB when the corpus contains less occurence of each verb. The verb-pair rules extracted from NB for causality extraction has the highest precision (0.88) with the recall of 0.75 from the plant disease corpus whereas from SVM has the highest precision (0.89) with the recall of 0.76 from bird flu news. For boundary determination, our methodology can handle very well with approximate 96% accuracy. In addition, the extracted causality results from this research can be generalized as laws in the Inductive-Statistical theory of Hempel's explanation theory, which will be useful for QA and reasoning.
Year
DOI
Venue
2007
10.1093/ietisy/e90-d.10.1523
IEICE Transactions
Keywords
Field
DocType
causality extraction,multiple edus,interesting causality event,highest precision,mining causality,causality edus,causality knowledge,causality result,question answering system,adjacent edus,main problem,verb-pair rule
Adjacency list,Causality,Computer science,Phrase,Natural language processing,Artificial intelligence,Verb,Question answering,Pattern recognition,Naive Bayes classifier,Support vector machine,Delimiter,Machine learning
Journal
Volume
Issue
ISSN
E90-D
10
1745-1361
Citations 
PageRank 
References 
10
0.82
6
Authors
2
Name
Order
Citations
PageRank
chaveevan pechsiri1195.58
asanee kawtrakul216125.90