@inproceedings{chen-etal-2016-using,
title = "Using {W}ikipedia and Semantic Resources to Find Answer Types and Appropriate Answer Candidate Sets in Question Answering",
author = "Chen, Po-Chun and
Zhuang, Meng-Jie and
Lin, Chuan-Jie",
editor = "Choi, Key-Sun and
Unger, Christina and
Vossen, Piek and
Kim, Jin-Dong and
Kando, Noriko and
Ngonga Ngomo, Axel-Cyrille",
booktitle = "Proceedings of the Open Knowledge Base and Question Answering Workshop ({OKBQA} 2016)",
month = dec,
year = "2016",
address = "Osaka, Japan",
publisher = "The COLING 2016 Organizing Committee",
url = "https://aclanthology.org/W16-4401",
pages = "1--10",
abstract = "This paper proposes a new idea that uses Wikipedia categories as answer types and defines candidate sets inside Wikipedia. The focus of a given question is searched in the hierarchy of Wikipedia main pages. Our searching strategy combines head-noun matching and synonym matching provided in semantic resources. The set of answer candidates is determined by the entry hierarchy in Wikipedia and the hyponymy hierarchy in WordNet. The experimental results show that the approach can find candidate sets in a smaller size but achieve better performance especially for ARTIFACT and ORGANIZATION types, where the performance is better than state-of-the-art Chinese factoid QA systems.",
}
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<abstract>This paper proposes a new idea that uses Wikipedia categories as answer types and defines candidate sets inside Wikipedia. The focus of a given question is searched in the hierarchy of Wikipedia main pages. Our searching strategy combines head-noun matching and synonym matching provided in semantic resources. The set of answer candidates is determined by the entry hierarchy in Wikipedia and the hyponymy hierarchy in WordNet. The experimental results show that the approach can find candidate sets in a smaller size but achieve better performance especially for ARTIFACT and ORGANIZATION types, where the performance is better than state-of-the-art Chinese factoid QA systems.</abstract>
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%0 Conference Proceedings
%T Using Wikipedia and Semantic Resources to Find Answer Types and Appropriate Answer Candidate Sets in Question Answering
%A Chen, Po-Chun
%A Zhuang, Meng-Jie
%A Lin, Chuan-Jie
%Y Choi, Key-Sun
%Y Unger, Christina
%Y Vossen, Piek
%Y Kim, Jin-Dong
%Y Kando, Noriko
%Y Ngonga Ngomo, Axel-Cyrille
%S Proceedings of the Open Knowledge Base and Question Answering Workshop (OKBQA 2016)
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F chen-etal-2016-using
%X This paper proposes a new idea that uses Wikipedia categories as answer types and defines candidate sets inside Wikipedia. The focus of a given question is searched in the hierarchy of Wikipedia main pages. Our searching strategy combines head-noun matching and synonym matching provided in semantic resources. The set of answer candidates is determined by the entry hierarchy in Wikipedia and the hyponymy hierarchy in WordNet. The experimental results show that the approach can find candidate sets in a smaller size but achieve better performance especially for ARTIFACT and ORGANIZATION types, where the performance is better than state-of-the-art Chinese factoid QA systems.
%U https://aclanthology.org/W16-4401
%P 1-10
Markdown (Informal)
[Using Wikipedia and Semantic Resources to Find Answer Types and Appropriate Answer Candidate Sets in Question Answering](https://aclanthology.org/W16-4401) (Chen et al., 2016)
ACL