Publication Type

Journal Article

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

Research Areas

Data Management and Analytics

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

Creative Commons License

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

Additional URL

http://doi.org/10.1145/2559157

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