Enhanced Answer Selection in CQA Using Multi-Dimensional Features Combination
Enhanced Answer Selection in CQA Using Multi-Dimensional Features Combination
复制标题
使用多维特征组合增强 CQA 中的答案选择
DOI：
10.26599/tst.2018.9010050
复制
发表时间：
2019-06-01
影响因子：
6.6
通讯作者：
Liu, Junfei
中科院分区：
文献类型：
--
作者：
Fan, Hongjie;Ma, Zhiyi;Liu, Junfei
网络论坛中的社区问答（Community Question Questioning，CQA）作为用户交流的经典论坛，与传统问答相比，能够提供大量高质量的有用答案。开发根据用户问题获得良好的，诚实的答案的方法是自然语言处理中的一项具有挑战性的任务。许多答案与实际问题无关或转移主题，这通常发生在相对较长的答案中。在本文中，我们加强CQA的答案选择，使用多维特征组合和相似性顺序。我们充分利用问题答案中的信息来判断问题和答案之间的相似度，并使用基于文本的答案描述来判断答案是否合理。我们的工作包括两个子任务：（a）将答案分类为好的、坏的或可能与问题相关的，以及（B）基于问题的所有答案的列表回答是/否。实验结果表明，我们的方法比基线模型更有效，与其他模型相比，其整体排名相对较高。
: Community Question Answering (CQA) in web forums, as a classic forum for user communication, provides a large number of high-quality useful answers in comparison with traditional question answering. Development of methods to get good, honest answers according to user questions is a challenging task in natural language processing. Many answers are not associated with the actual problem or shift the subjects, and this usually occurs in relatively long answers. In this paper, we enhance answer selection in CQA using multi-dimensional feature combination and similarity order. We make full use of the information in answers to questions to determine the similarity between questions and answers, and use the text-based description of the answer to determine whether it is a reasonable one. Our work includes two subtasks: (a) classifying answers as good, bad, or potentially associated with a question, and (b) answering YES/NO based on a list of all answers to a question. The experimental results show that our approach is significantly more efficient than the baseline model, and its overall ranking is relatively high in comparison with that of other models.