关联模式挖掘与词向量学习融合的伪相关反馈查询扩展
Pseudo-Relevance Feedback Query Expansion Based on the Fusion of Association Pattern Mining and Word Embedding Learning
- 电子学报 2021年49卷第7期 页码：1305-1313
- 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：
[ "黄名选 男，1966年出生于广西乐业县，硕士，现为广西财经学院教授，硕士研究生导师，主要研究方向为数据挖掘、信息检索、机器学习，主持国家自然科学基金项目2项，主持完成广西自然科学基金项目1项和广西教育厅科研项目3项，获2011年广西高校优秀人才资助计划项目1项，参与完成国家自然科学基金项目1项，发表学术论文60余篇，其中，中文核心期刊论文40余篇，期刊EI收录7篇，ISTP收录1篇，发明专利授权15件.E⁃mail: mingxh05@163.com" ]
- 基金信息：国家自然科学基金(61762006)
DOI：10.12263/DZXB.20200654
中图分类号： TP311收稿：2020-07-05，
修回：2020-09-25，
纸质出版：2021-07-25
- 稿件说明：
移动端阅览
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.
关键词
Keywords
references
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