research-article

Learning to Recommend Descriptive Tags for Questions in Social Forums

Authors:

Tat-Seng ChuaAuthors Info & Claims

ACM Transactions on Information Systems (TOIS), Volume 32, Issue 1

Article No.: 5, Pages 1 - 23

https://doi.org/10.1145/2559157

Published: 01 January 2014 Publication History

Abstract

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.

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Index Terms

Learning to Recommend Descriptive Tags for Questions in Social Forums
1. Information systems
  1. Information retrieval
    1. Document representation

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Published In

cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 32, Issue 1

January 2014

123 pages

ISSN:1046-8188

EISSN:1558-2868

DOI:10.1145/2576772

Editor:
Jamie Callan
Carnegie Mellon University, USA

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Copyright © 2014 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 January 2014

Accepted: 01 November 2013

Revised: 01 September 2013

Received: 01 February 2013

Published in TOIS Volume 32, Issue 1

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Chen HLi J(2024)Precisely Simulating Human Observers: A Graphical Perceptual Learning for Understanding Medical PicturesIEEE Access10.1109/ACCESS.2024.346074212(133606-133616)Online publication date: 2024
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https://dl.acm.org/doi/10.1145/3578932
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