Community question answering (cQA) has emerged as a popular service on the web; users can use it to ask and answer questions and access historical question-answer (QA) pairs. cQA retrieval, as an alternative to general web searches, has several advantages. First, user can register a query in the form of natural language sentences instead of a set of keywords; thus, they can present the required information more clearly and comprehensively. Second, the system returns several possible answers instead of a long list of ranked documents, thereby enhancing the efficient location of the desired answers. Question retrieval from a cQA archive, an essential function of cQA retrieval services, aims to retrieve historical QA pairs relevant to the query question. In this study, combined queries (combined inverted and nextword indexes) are proposed for question retrieval in cQA. The method performance is investigated for two different scenarios: (a) when only questions from QA pairs are used as documents, and (b) when QA pairs are used as documents. In the proposed method, combined indexes are first created for both queries and documents; then, different information retrieval (IR) models are used to retrieve relevant questions from the cQA archive. Evaluation is performed on a public Yahoo! Answers dataset; the results thereby obtained show that using combined queries for all three IR models (vector space model, Okapi model, and language model) improves performance in terms of the retrieval precision and ranking effectiveness. Notably, by using combined indexes when both QA pairs are used as documents, the retrieval and ranking effectiveness of these cQA retrieval models increases significantly.

中文翻译：

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在社区问答中使用组合查询进行问题检索

社区问答 (cQA) 已成为网络上的一项流行服务；用户可以使用它来提问和回答问题并访问历史问答 (QA) 对。cQA 检索作为一般网络搜索的替代方案，具有几个优点。首先，用户可以以自然语言句子的形式而不是一组关键字来注册查询；因此，他们可以更清晰、更全面地呈现所需信息。其次，系统会返回几个可能的答案，而不是一长串排名文档，从而提高了对所需答案的有效定位。从 cQA 档案中检索问题是 cQA 检索服务的一项基本功能，旨在检索与查询问题相关的历史 QA 对。在这项研究中，组合查询（组合倒排和下一个词索引）被提议用于 cQA 中的问题检索。针对两种不同场景研究了该方法的性能：（a）仅将 QA 对中的问题用作文档时，以及（b）当 QA 对用作文档时。在所提出的方法中，首先为查询和文档创建组合索引；然后，使用不同的信息检索 (IR) 模型从 cQA 档案中检索相关问题。评估是在公共 Yahoo! 答案数据集；由此获得的结果表明，对所有三个 IR 模型（向量空间模型、Okapi 模型和语言模型）使用组合查询提高了检索精度和排名效率方面的性能。值得注意的是，当两个 QA 对都用作文档时，通过使用组合索引，