Combining Q&A Pair Quality and Question Relevance Features on Community-based Question Retrieval

TL;DR

This paper introduces a novel community-based question retrieval method that combines question and answer quality with question relevance, utilizing an improved term weighting model and CNNs, leading to significant performance improvements.

Contribution

It proposes a new question retrieval approach that integrates question-answer quality features with relevance, enhancing retrieval accuracy over existing models.

Findings

The proposed methods increase MAP by 4.91% and 6.31%.

Combining quality and relevance features improves retrieval performance.

The CNN-based method outperforms traditional models.

Abstract

The Q&A community has become an important way for people to access knowledge and information from the Internet. However, the existing translation based on models does not consider the query specific semantics when assigning weights to query terms in question retrieval. So we improve the term weighting model based on the traditional topic translation model and further considering the quality characteristics of question and answer pairs, this paper proposes a communitybased question retrieval method that combines question and answer on quality and question relevance (T2LM+). We have also proposed a question retrieval method based on convolutional neural networks. The results show that Compared with the relatively advanced methods, the two methods proposed in this paper increase MAP by 4.91% and 6.31%.