NDLI: Evaluating Causal Questions for Question Answering

Content Provider	IEEE Xplore Digital Library
Author	Vazquez-Reyes, S. Black, W.J.
Copyright Year	2008
Description	Author affiliation: Sch. of Comput. Sci., Univ. of Manchester, Manchester (Vazquez-Reyes, S.; Black, W.J.)
Abstract	Question answering systems involve the extraction of answers to a question rather than retrieval of relevant documents. For question answering evaluation, it is necessary that a human assessor decide the correctness of the answers, given that the same answer can be expressed in different ways. Therefore, the use of suitable test collection could help to identify where the systems are performing well, or where they are failing. Our data collection analysis suggests that there should be a proportion of text in which the reasoning or explanation that constitutes an answer to a ldquowhyrdquo question is present in, or capable of extracting from, the source text. We report on an implemented component for the extraction of candidate answers from source text. This component uses an approach that combines lexical overlapping and lexical semantic relatedness (lexico-syntactic approach) for ranking possible answers to causal questions. On undifferentiated texts, we obtain an overall recall of 34.13% indicating that simple match is adequate for answering over 1/3 of ldquowhyrdquo questions. We have analyzed those question-answer pairs units where the answer is explicit, ambiguous and implicit, and shown that if we can separate the last category, the rate of recall increases considerably.
Starting Page	132
Ending Page	142
File Size	182751
Page Count	11
File Format	PDF
ISBN	9780769534398
ISSN	15504069
DOI	10.1109/ENC.2008.14
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-10-06
Publisher Place	Mexico
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Data mining Cognition Gold Drugs Computer science Humans Cities and towns Causal Questions Question Answering Natural Language Processing
Content Type	Text
Resource Type	Article

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