Learning to recommend descriptive tags for questions in social forums

Around 40% of the questions in the emerging social-oriented question answering forums have at most one manually labeled tag, which is caused by incomprehensive question understanding or informal tagging behaviors. The incompleteness of question tags severely hinders all the tag-based manipulations,...

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Main Authors: NIE, Liqiang, ZHAO, Yiliang, WANG, Xiangyu, SHEN, Jialie, CHUA, Tat-Seng
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Language:English
Published: Institutional Knowledge at Singapore Management University 2014
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Online Access:https://ink.library.smu.edu.sg/sis_research/1963
https://ink.library.smu.edu.sg/context/sis_research/article/2962/viewcontent/a5_nie.pdf
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Institution: Singapore Management University
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spelling sg-smu-ink.sis_research-29622017-03-03T07:29:29Z Learning to recommend descriptive tags for questions in social forums NIE, Liqiang ZHAO, Yiliang WANG, Xiangyu SHEN, Jialie CHUA, Tat-Seng Around 40% of the questions in the emerging social-oriented question answering forums have at most one manually labeled tag, which is caused by incomprehensive question understanding or informal tagging behaviors. The incompleteness of question tags severely hinders all the tag-based manipulations, such as feeds for topic-followers, ontological knowledge organization, and other basic statistics. This article presents a novel scheme that is able to comprehensively learn descriptive tags for each question. Extensive evaluations on a representative real-world dataset demonstrate that our scheme yields significant gains for question annotation, and more importantly, the whole process of our approach is unsupervised and can be extended to handle large-scale data. 2014-01-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/1963 info:doi/10.1145/2559157 https://ink.library.smu.edu.sg/context/sis_research/article/2962/viewcontent/a5_nie.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Knowledge organization Question annotation Social QA Databases and Information Systems
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Knowledge organization
Question annotation
Social QA
Databases and Information Systems
spellingShingle Knowledge organization
Question annotation
Social QA
Databases and Information Systems
NIE, Liqiang
ZHAO, Yiliang
WANG, Xiangyu
SHEN, Jialie
CHUA, Tat-Seng
Learning to recommend descriptive tags for questions in social forums
description Around 40% of the questions in the emerging social-oriented question answering forums have at most one manually labeled tag, which is caused by incomprehensive question understanding or informal tagging behaviors. The incompleteness of question tags severely hinders all the tag-based manipulations, such as feeds for topic-followers, ontological knowledge organization, and other basic statistics. This article presents a novel scheme that is able to comprehensively learn descriptive tags for each question. Extensive evaluations on a representative real-world dataset demonstrate that our scheme yields significant gains for question annotation, and more importantly, the whole process of our approach is unsupervised and can be extended to handle large-scale data.
format text
author NIE, Liqiang
ZHAO, Yiliang
WANG, Xiangyu
SHEN, Jialie
CHUA, Tat-Seng
author_facet NIE, Liqiang
ZHAO, Yiliang
WANG, Xiangyu
SHEN, Jialie
CHUA, Tat-Seng
author_sort NIE, Liqiang
title Learning to recommend descriptive tags for questions in social forums
title_short Learning to recommend descriptive tags for questions in social forums
title_full Learning to recommend descriptive tags for questions in social forums
title_fullStr Learning to recommend descriptive tags for questions in social forums
title_full_unstemmed Learning to recommend descriptive tags for questions in social forums
title_sort learning to recommend descriptive tags for questions in social forums
publisher Institutional Knowledge at Singapore Management University
publishDate 2014
url https://ink.library.smu.edu.sg/sis_research/1963
https://ink.library.smu.edu.sg/context/sis_research/article/2962/viewcontent/a5_nie.pdf
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