Searching Questions by Identifying Question Topic and Question Focus
Searching Questions by Identifying Question Topic and Question Focus
复制标题
DOI：
--
复制
发表时间：
2008-06
期刊：
影响因子：
--
通讯作者：
Huizhong Duan;Yunbo Cao;Chin-Yew Lin;Yong Yu
中科院分区：
文献类型：
--
作者：
Huizhong Duan;Yunbo Cao;Chin-Yew Lin;Yong Yu
本文涉及问题搜索问题。在有问题的搜索中，考虑到一个问题，我们将在语义上等同或接近查询问题返回问题。在本文中，我们建议通过确定问题主题和问题重点来进行问题搜索。更具体地说，我们首先在包括问题主题和问题重点的数据结构中总结问题。然后，我们将问题主题和问题重点建模为语言建模框架以进行搜索。我们还建议使用基于MDLB的树状模型来识别问题主题，并自动焦点。实验结果表明，我们识别问题主题和搜索问题重点的方法显着胜过基线方法，例如向量空间模型（VSM）和信息检索的语言模型（LMIR）。
This paper is concerned with the problem of question search. In question search, given a question as query, we are to return questions semantically equivalent or close to the queried question. In this paper, we propose to conduct question search by identifying question topic and question focus. More specifically, we first summarize questions in a data structure consisting of question topic and question focus. Then we model question topic and question focus in a language modeling framework for search. We also propose to use the MDLbased tree cut model for identifying question topic and question focus automatically. Experimental results indicate that our approach of identifying question topic and question focus for search significantly outperforms the baseline methods such as Vector Space Model (VSM) and Language Model for Information Retrieval (LMIR).