Learning to recommend descriptive tags for questions in social forums

Author

Liqiang NIE, National University of Singapore
Yiliang ZHAO, National University of Singapore
Xiangyu WANG, National University of Singapore
Jialie SHEN, Singapore Management UniversityFollow
Tat-Seng CHUA, National University of Singapore

Publication Type

Journal Article

Version

acceptedVersion

Publication Date

1-2014

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.

Keywords

Knowledge organization, Question annotation, Social QA

Discipline

Databases and Information Systems

Publication

ACM Transactions on Information Systems

Volume

32

Issue

1

First Page

1

Last Page

23

ISSN

1046-8188

Identifier

10.1145/2559157

Publisher

ACM

Citation

NIE, Liqiang; ZHAO, Yiliang; WANG, Xiangyu; SHEN, Jialie; and CHUA, Tat-Seng. Learning to recommend descriptive tags for questions in social forums. (2014). ACM Transactions on Information Systems. 32, (1), 1-23.
Available at: https://ink.library.smu.edu.sg/sis_research/1963

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.

Additional URL

http://doi.org/10.1145/2559157