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首页> 外文期刊>Journal of Intelligent Information Systems >Efficient question classification and retrieval using category information and word embedding on cQA services
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Efficient question classification and retrieval using category information and word embedding on cQA services

机译：使用类别信息和嵌入CQA服务的有效问题分类和检索

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摘要

Classifying the task of automatically assigning unlabeled questions into predefined categories (or topics) and effectively retrieving a similar question are crucial aspects of an effective cQA service. We first address the problems associated with estimating and utilizing the distribution of words in each category of word weights. We then apply an automatic expansion word generation technique that is based on our proposed weighting method and the pseudo relevance feedback to question classification. Secondly to address the lexical gap problem in question retrieval, the case frame of the sentence is first defined using the extracted components of a sentence, and a similarity measure based on the case frame and the word embedding is then derived to determine the similarities between two sentences. These similarities are then used to reorder the results of the first retrieval model. Consequently, the proposed methods significantly improve the performance of question classification and retrieval.
机译：对自动将未标记的问题分配成预定义类别（或主题）并有效地检索类似问题的任务是有效CQA服务的关键方面。我们首先解决与估计和利用单词权重中的单词分布相关的问题。然后，我们应用自动扩展词生成技术，该技术基于我们所提出的加权方法和伪相关反馈来提出问题分类。其次，为了解决问题检索的词汇形态问题，首先使用句子的提取组件来定义句子的壳体帧，然后导出基于案例帧和单词嵌入的相似度量来确定两个之间的相似度句子。然后使用这些相似之处重新排序第一检索模型的结果。因此，所提出的方法显着提高了问题分类和检索的性能。

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