Answer Keyword Generation for Community Question Answering by Multi-aspect Gamma-Poisson Matrix Completion

Liu, Q; Do, TDT; Cao, L

Answer Keyword Generation for Community Question Answering by Multi-aspect Gamma-Poisson Matrix Completion

Liu, Q Do, TDT Cao, L

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Intelligent Systems, 2020, PP, (99), pp. 1-1
Issue Date:: 2020-01-01

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Field	Value	Language
dc.contributor.author	Liu, Q
dc.contributor.author	Do, TDT
dc.contributor.author	Cao, L https://orcid.org/0000-0003-1562-9429
dc.date.accessioned	2020-10-31T21:05:48Z
dc.date.available	2020-10-31T21:05:48Z
dc.date.issued	2020-01-01
dc.identifier.citation	IEEE Intelligent Systems, 2020, PP, (99), pp. 1-1
dc.identifier.issn	1541-1672
dc.identifier.issn	1941-1294
dc.identifier.uri	http://hdl.handle.net/10453/143652
dc.description.abstract	IEEE Community question answering (CQA) recommends appropriate answers to existing and new questions. Such answer recommendation is challenging since CQA data is often sparse and decentralized and lacks sufficient information to generate suitable answers to existing questions. Matching answers to new questions is more challenging in modeling Q/A sparsity, generating answers to cold-start/novel questions, and integrating metadata about Q/A into models, etc. This paper addresses these issues by a novel statistical model to automatically generate answer keywords in CQA with multi-aspect Gamma-Poisson matrix completion (MAGIC). MAGIC is the first trial in CQA to model multiple aspects of Q/A sentence information in CQA by involving Q/A metadata, Q/A sparsity, and both lexical and semantic Q/A information in a hierarchical Gamma-Poisson model. MAGIC can efficiently generate answer keywords for both existing and new questions against nonnegative matrix factorization (MF), probability MF, and relevant Poisson factorization models w.r.t. recommending appropriate and informative answer keywords.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation	http://purl.org/au-research/grants/arc/DP190101079
dc.relation.ispartof	IEEE Intelligent Systems
dc.relation.isbasedon	10.1109/MIS.2020.2997714
dc.rights	© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0806 Information Systems, 0906 Electrical and Electronic Engineering
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Answer Keyword Generation for Community Question Answering by Multi-aspect Gamma-Poisson Matrix Completion
dc.type	Journal Article
utslib.citation.volume	PP
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0806 Information Systems
utslib.for	0906 Electrical and Electronic Engineering
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
dc.date.updated	2020-10-31T21:05:20Z
pubs.issue	99
pubs.publication-status	Published
pubs.volume	PP
utslib.citation.issue	99

Abstract:

IEEE Community question answering (CQA) recommends appropriate answers to existing and new questions. Such answer recommendation is challenging since CQA data is often sparse and decentralized and lacks sufficient information to generate suitable answers to existing questions. Matching answers to new questions is more challenging in modeling Q/A sparsity, generating answers to cold-start/novel questions, and integrating metadata about Q/A into models, etc. This paper addresses these issues by a novel statistical model to automatically generate answer keywords in CQA with multi-aspect Gamma-Poisson matrix completion (MAGIC). MAGIC is the first trial in CQA to model multiple aspects of Q/A sentence information in CQA by involving Q/A metadata, Q/A sparsity, and both lexical and semantic Q/A information in a hierarchical Gamma-Poisson model. MAGIC can efficiently generate answer keywords for both existing and new questions against nonnegative matrix factorization (MF), probability MF, and relevant Poisson factorization models w.r.t. recommending appropriate and informative answer keywords.

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http://hdl.handle.net/10453/143652