通过词相似度与问题相似度扩展查询，缓解问答平台中的词汇鸿沟问题。
QEQR: An Exploration of Query Expansion Methods for Question Retrieval in CQA Services
- 融合词相似度与问题相似度进行查询扩展
- 最佳方法相对基线提升1.8%的检索效果
- 适合需要精准问答检索的应用场景
CQA服务是满足用户信息需求的重要知识来源。在这些服务中，问题检索旨在通过找到与用户问题相似的问题来帮助其获取答案。然而，相关问题之间的词汇差异会阻碍相似性匹配。为此，本文采用查询扩展方法，结合基于词相似度的方法，提出一种基于问题相似度的扩展策略，并引入选择性扩展机制，以缓解词汇鸿沟。实验表明，最优方法在不使用查询扩展的基线基础上实现了1.8%的显著相对提升。
原文摘要 · Abstract (English)
CQA services are valuable sources of knowledge that can be used to find answers to users' information needs. In these services, question retrieval aims to help users with their information needs by finding similar questions to theirs. However, finding similar questions is obstructed by the lexical gap that exists between relevant questions. In this work, we target this problem by using query expansion methods. We use word-similarity-based methods, propose a question-similarity-based method and selective expansion of these methods to expand a question that's been submitted and mitigate the lexical gap problem. Our best method achieves a significant relative improvement of 1.8\% compared to the best-performing baseline without query expansion.
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