Capturing the Semantics of Key Phrases Using Multiple Languages for Question Retrieval
Capturing the Semantics of Key Phrases Using Multiple Languages for Question Retrieval
复制标题
使用多种语言捕获关键短语的语义进行问题检索
DOI：
10.1109/tkde.2015.2502944
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发表时间：
2016-04-01
影响因子：
8.9
通讯作者：
Chua, Tat-Seng
中科院分区：
文献类型：
--
作者：
Zhang, Wei-Nan;Ming, Zhao-Yan;Chua, Tat-Seng
在Web 2.0时代，社区用户贡献的问答为通过网络搜索获取知识提供了一种重要的替代方式。当前基于社区的问答（CQA）服务中的问题检索通常对长而复杂的查询（比如一些问题）效果不佳。主要原因是自然语言查询的冗长以及检索过程中查询与CQA档案中的候选问题之间的词汇不匹配。为了解决这两个问题，现有的解决方案试图通过区分查询中的关键概念并用相关内容扩展查询来优化搜索查询。然而，使用现有的查询优化方法只能识别关键概念和非关键概念，而关键概念之间的差异被忽视了。此外，现有的查询扩展方法不仅忽视了查询中关键概念的权重，而且没有考虑对其进行概念层面的扩展。在本文中，我们探索了一种用于查询优化的关键概念识别方法以及一种基于枢纽语言翻译的方法来探索关键概念的释义。我们进一步提出了一种新的问题检索模型，该模型能够无缝集成关键概念及其释义。实验结果表明，集成检索模型在问题检索方面显著优于现有最先进的模型。
In the age of Web 2.0, community user contributed questions and answers provide an important alternative for knowledge acquisition through web search. Question retrieval in current community-based question answering (CQA) services do not, in general, work well for long and complex queries, such as the questions. The main reasons are the verboseness in natural language queries and the word mismatch between the queries and the candidate questions in the CQA archive during retrieval. To address these two problems, existing solutions try to refine the search queries by distinguishing the key concepts in the queries and expanding the queries with relevant content. However, using the existing query refinement approaches can only identify the key and non-key concepts, while the differences between the key concepts are overlooked. Moreover, the existing query expansion approaches, not only overlook the weights of key concepts in the queries, but also fail to consider concept level expansion for them. In this paper, we explore a key concept identification approach for query refinement and a pivot language translation based approach to explore key concept paraphrasing. We further propose a new question retrieval model which can seamlessly integrate the key concepts and their paraphrases. The experimental results demonstrate that the integrated retrieval model significantly outperforms the state-of-the-art models in question retrieval.