Query Expansion for Exploratory Search with Subtopic Discovery in Community Question Answering

被引：0

作者：

Gao, Li ^{[1
]}

Lu, Yao ^{[1
]}

Zhang, Qin ^{[2
]}

Yang, Hong ^{[3
]}

Hu, Yue ^{[1
]}

机构：

[1] Chinese Acad Sci, Inst Informat Engn, Beijing, Peoples R China

[2] Univ Technol Sydney, Ctr Quantum Computat & Intelligent Syst, Sydney, NSW, Australia

[3] MathWorks, Beijing 100090, Peoples R China

来源：

2016 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) | 2016年

关键词：

Exploratory search; Query expansion; Community Question Answering; Subtopic mining;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Exploratory search is cumbersome with today's search engines, where a user aims to better understand complex concepts. Query expansions techniques have been widely used in exploratory search. However, query expansions often recommend queries that differ from the user's search intentions due to different contexts. Yet, many of users' needs could be addressed by asking people via popular Community Question Answering (CQA) services. In this paper, we investigate query expansion techniques for exploratory search using the resources of CQA to discover the user's search intentions. Specifically, we denote the explicit intuition as the subtopic that supports the user's exploratory task. We propose the method CqaQuExp to mine the subtopics, which mainly contains three subtask: Question retrieval, where we extract the questions and corresponding answers from CQA; subtopic mining, where we discover the subtopics based on the extracted information; Candidate concepts discovery, where we select the candidate concepts from the discovered subtopics for query expansion. Experimental results on realworld data from Yahoo! Answers demonstrate the effectiveness of the proposed methods.

引用

页码：4715 / 4720

页数：6

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