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Efficient question classification and retrieval using category information and word embedding on cQA services

机译:在cQA服务上使用类别信息和单词嵌入进行有效的问题分类和检索

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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:将自动分配未标记问题的任务分类为预定义类别(或主题)并有效检索相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每个单词权重类别中的单词分布相关的问题。然后,我们基于建议的加权方法和伪相关反馈将自动扩展词生成技术应用于问题分类。其次,为了解决问题检索中的词汇间隙问题,首先使用提取出的句子成分来定义句子的格框架,然后基于格框架和词嵌入得出相似度度量,以确定两者之间的相似度句子。然后使用这些相似性对第一个检索模型的结果进行重新排序。因此,提出的方法大大提高了问题分类和检索的性能。

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