Pseudo-Relevance Feedback Query Expansion Based on the Fusion of Association Pattern Mining and Word Embedding Learning

HUANG Ming-xuan

doi:10.12263/DZXB.20200654

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Pseudo-Relevance Feedback Query Expansion Based on the Fusion of Association Pattern Mining and Word Embedding Learning

PAPERS | 更新时间：2025-12-08

- Pseudo-Relevance Feedback Query Expansion Based on the Fusion of Association Pattern Mining and Word Embedding Learning
- ACTA ELECTRONICA SINICA Vol. 49, Issue 7, Pages: 1305-1313(2021)
- 作者机构：
  
  1.广西跨境电商智能信息处理重点实验室 (广西财经学院), 广西南宁 530003
  2.广西财经学院信息与统计学院, 广西南宁 530003
- 作者简介：
- 基金信息：
- DOI：10.12263/DZXB.20200654
  CLC： TP311
- Received：05 July 2020，
  
  Revised：2020-09-25，
  
  Published：25 July 2021
- 稿件说明：
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黄名选.关联模式挖掘与词向量学习融合的伪相关反馈查询扩展[J].电子学报,2021,49(07):1305-1313.

HUANG Ming-xuan.Pseudo-Relevance Feedback Query Expansion Based on the Fusion of Association Pattern Mining and Word Embedding Learning[J].ACTA ELECTRONICA SINICA,2021,49(07):1305-1313.
黄名选.关联模式挖掘与词向量学习融合的伪相关反馈查询扩展[J].电子学报,2021,49(07):1305-1313. DOI： 10.12263/DZXB.20200654.

HUANG Ming-xuan.Pseudo-Relevance Feedback Query Expansion Based on the Fusion of Association Pattern Mining and Word Embedding Learning[J].ACTA ELECTRONICA SINICA,2021,49(07):1305-1313. DOI： 10.12263/DZXB.20200654.

摘要

针对自然语言处理中查询主题漂移和词不匹配问题，提出基于CSC(Copulas-based Support and Confidence)框架的关联模式挖掘与规则扩展算法，并将基于统计学分析的关联模式与具有上下文语义信息的词向量融合，提出关联模式挖掘与词向量学习融合的伪相关反馈查询扩展模型.该模型对伪相关反馈文档集挖掘规则扩展词，对初检文档集进行词嵌入学习训练得到词向量，计算规则扩展词与原查询的向量相似度，提取向量相似度不低于阈值的规则扩展词作为最终扩展词.实验结果表明，所提扩展模型能有效地减少查询主题漂移和词不匹配问题，提高检索性能，与现有基于关联模式的和基于词向量的查询扩展方法比较，MAP（Mean Average Precision）平均增幅最大可达17.52%，对短查询更有效.所提挖掘方法可用于其他文本挖掘任务和推荐系统，以提高其性能.

Abstract

In order to solve the problems of query topic drift and word mismatch in natural language processing

an algorithm of association pattern mining and rule expansion based on CSC(Copulas-based Support and Confidence) framework is proposed. The association patterns based on statistical analysis are fused with the word embedding with context semantic information

and a pseudo-relevance feedback query expansion model is presented based on the fusion of association pattern mining and word embedding learning. In this model

the rule expansion terms are mined from the pseudo-relevance feedback document set

and the word vectors are obtained by word embedding learning training of the initial document set. The vector similarity between the rule expansion term and original query is calculated

and the rule expansion terms whose vector similarity is not lower than the threshold are extracted as the final expansion terms. The experimental results show that the proposed expansion model can effectively reduce the problems of query topic drift and word mismatch

improving the performance of information retrieval. Compared with the existing query expansion methods based on association pattern and word embedding

the average increase of the MAP（Mean Average Precision）of the proposed expansion model is up to 17.52%. The expansion model in this paper is more effective for short queries. The proposed mining method can be used in other text mining tasks and recommendation systems to improve their performance.

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