An enhanced convolutional neural network model for answer selection

Guo, J; Yue, B; Xu, G; Yang, Z; Wei, JM

An enhanced convolutional neural network model for answer selection

Guo, J Yue, B Xu, G

Yang, Z Wei, JM

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Publication Type:: Conference Proceeding
Citation:: 26th International World Wide Web Conference 2017, WWW 2017 Companion, 2019, pp. 789 - 790
Issue Date:: 2019-01-01

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Field	Value	Language
dc.contributor.author	Guo, J	en_US
dc.contributor.author	Yue, B	en_US
dc.contributor.author	Xu, G https://orcid.org/0000-0003-4493-6663	en_US
dc.contributor.author	Yang, Z	en_US
dc.contributor.author	Wei, JM	en_US
dc.date.issued	2019-01-01	en_US
dc.identifier.citation	26th International World Wide Web Conference 2017, WWW 2017 Companion, 2019, pp. 789 - 790	en_US
dc.identifier.isbn	9781450349147	en_US
dc.identifier.uri	http://hdl.handle.net/10453/105701
dc.description.abstract	© 2017 International World Wide Web Conference Committee (IW3C2), published under Creative Commons CC BY 4.0 License. Answer selection is an important task in question answering (QA) from the Web. To address the intrinsic difficulty in encoding sentences with semantic meanings, we introduce a general framework, i.e., Lexical Semantic Feature based Skip Convolution Neural Network (LSF-SCNN), with several optimization strategies. The intuitive idea is that the granular representations with more semantic features of sentences are deliberately designed and estimated to capture the similarity between question-answer pairwise sentences. The experimental results demonstrate the effectiveness of the proposed strategies and our model outperforms the state-of-the-art ones by up to 3.5% on the metrics of MAP and MRR.	en_US
dc.relation.ispartof	26th International World Wide Web Conference 2017, WWW 2017 Companion	en_US
dc.relation.isbasedon	10.1145/3041021.3054216	en_US
dc.title	An enhanced convolutional neural network model for answer selection	en_US
dc.type	Conference Proceeding
utslib.for	0805 Distributed Computing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US

Abstract:

© 2017 International World Wide Web Conference Committee (IW3C2), published under Creative Commons CC BY 4.0 License. Answer selection is an important task in question answering (QA) from the Web. To address the intrinsic difficulty in encoding sentences with semantic meanings, we introduce a general framework, i.e., Lexical Semantic Feature based Skip Convolution Neural Network (LSF-SCNN), with several optimization strategies. The intuitive idea is that the granular representations with more semantic features of sentences are deliberately designed and estimated to capture the similarity between question-answer pairwise sentences. The experimental results demonstrate the effectiveness of the proposed strategies and our model outperforms the state-of-the-art ones by up to 3.5% on the metrics of MAP and MRR.

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http://hdl.handle.net/10453/105701