An approach to answer selection in question-answering based on semantic relations
Pages 1852 - 1857
Abstract
A usual strategy to select the final answer in factoid Question-Answering (QA) relies on redundancy. A score is given to each candidate answer as a function of its frequency of occurrence, and the final answer is selected from the set of candidates sorted in decreasing order of score. For that purpose, systems often try to group together semantically equivalent answers. However, they hold several other semantic relations, such as inclusion, which are not considered, and candidates are mostly seen independently, as competitors.
Our hypothesis is that not just equivalence, but other relations between candidate answers have impact on the performance of a redundancy-based QA system. In this paper, we describe experimental studies to back up this hypothesis. Our findings show that, with relatively simple techniques to recognize relations, systems' accuracy can be improved for answers of categories NUMBER, DATE and ENTITY.
References
[1]
E. Brill, J. Lin, M. Banko, S. Dumais, and A. Ng. Data-intensive question answering. In Proc. 10th Text REtrieval Conference (TREC), pages 393-400, 2001.
[2]
S. Buchholz and W. Daelemans. Complex answers: a case study using a www question answering system. Nat. Lang. Eng., 7:301-323, 2001.
[3]
J. Chu-Carroll, K. Czuba, J. Prager, and A. Ittycheriah. In question answering, two heads are better than one. In Proc. NAACL'03, pages 24-31. ACL, 2003.
[4]
C. Clarke, G. Cormack, and T. Lynam. Exploiting redundancy in question answering. In Proc. of SIGIR, pages 358-365. ACM Press, 2001.
[5]
T. Dalmas and B. Webber. Answer comparison in automated question answering. Journal of Applied Logic, pages 104-120, 2007.
[6]
C. Fellbaum. WordNet: An Electronic Lexical Database. MIT Press, 1998.
[7]
C. Kwok, O. Etzioni, and D. Weld. Scaling question answering to the web. ACM Trans. Inf. Syst., 19:242-262, 2001.
[8]
X. Li and D. Roth. Learning question classifiers. In Proc. 19th Int. Conf. Computational linguistics, pages 1-7. ACL, 2002.
[9]
V. Moriceau. Answer generation with temporal data integration. In Proc. 10th European Workshop Nat. Lang. Generation (ENLG-05), pages 197-202, 2005.
[10]
V. Moriceau. Numerical data integration for cooperative question-answering. In Proc. Workshop KRAQ'06 on Knowledge and Reasoning for Language Processing, KRAQ '06, pages 42-49. ACL, 2006.
[11]
V. Moriceau. Intégration de données dans un système question-réponse sur le Web. PhD thesis, Université Paul Sabatier, 2007.
[12]
João Silva, Luísa Coheur, Ana Mendes, and Andreas Wichert. From symbolic to sub-symbolic information in question classification. Artificial Intelligence Review, 35:137-154, 2011. 10.1007/s10462-010-9188-4.
[13]
A. Téllez-Valero, M. Montes-Gómez, L. Villaseñor Pineda, and A. Peñas. Towards Multi-Stream Question Answering using Answer Validation. Informatica. Special Issue on Computational Linguistics and its Applications, 34(1), 2010.
[14]
B. Webber, C. Gardent, and J. Bos. Position statement: Inference in question answering. In Proc. LRECWorkshop on Question Answering: Strategy and Resources, pages 19-25, 2003.
Recommendations
An Answer Fusion Model for Web-based Question Answering
SKG '05: Proceedings of the First International Conference on Semantics, Knowledge and GridMany question answering systems are now focusing on the research of question-based answer mining from the Web information or snippets returned by the search engine. However, their main goal is only to get the related answers instead of the correct ...
Fuzzy-Based Answer Ranking in Question Answering Communities
Owing to the vast amount of information readily available on the World Wide Web, there has been a significant increase in the number of online question answering QA systems. A branch of QA systems that has seen such remarkable growth is the community-...
Combining evidence with a probabilistic framework for answer ranking and answer merging in question answering
Question answering (QA) aims at finding exact answers to a user's question from a large collection of documents. Most QA systems combine information retrieval with extraction techniques to identify a set of likely candidates and then utilize some ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
Sponsors
- The International Joint Conferences on Artificial Intelligence, Inc. (IJCAI)
Publisher
AAAI Press
Publication History
Published: 16 July 2011
Qualifiers
- Article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 14Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 23 Dec 2024