Learning to Recommend Descriptive Tags for Questions in Social Forums

被引：64

作者：

Nie, Liqiang ^{[1
]}

Zhao, Yi-Liang ^{[1
]}

Wang, Xiangyu ^{[1
]}

Shen, Jialie ^{[2
]}

Chua, Tat-Seng ^{[1
]}

机构：

[1] Natl Univ Singapore, Singapore 117548, Singapore

[2] Singapore Management Univ, Singapore 178902, Singapore

来源：

ACM TRANSACTIONS ON INFORMATION SYSTEMS | 2014年 / 32卷 / 01期

关键词：

Experimentation; Performance; Question annotation; social QA; knowledge organization;

D O I：

10.1145/2559157

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

080201 [机械制造及其自动化];

摘要：

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.

引用

页数：23