一种基于排序学习方法的查询扩展技术

查询扩展作为一门重要的信息检索技术,是以用户查询为基础,通过一定策略在原始查询中加入一些相关的扩展词,从而使得查询能够更加准确地描述用户信息需求。排序学习方法利用机器学习的知识构造排序模型对数据进行排序,是当前机器学习与信息检索交叉领域的研究热点。该文尝试利用伪相关反馈技术,在查询扩展中引入排序学习算法,从文档集合中提取与扩展词相关的特征,训练针对于扩展词的排序模型,并利用排序模型对新查询的扩展词集合进行重新排序,将排序后的扩展词根据排序得分赋予相应的权重,加入到原始查询中进行二次检索,从而提高信息检索的准确率。在TREC数据集合上的实验结果表明,引入排序学习算法有助于提高伪相关反馈的检索性能。

A Query Expansion Method Based on Learning to Rank

Query Expansion is an important technique for improving retrieval performance. It uses some strategies to add some relevant terms to the original query submitted by the user, which could express the user’s information need more exactly and completely. Learning to rank is a hot machine learning issue addressed in in information retrieval, seeking to automatically construct ranking models determining the relevance degrees between objects. This paper attempts to improve pseudo-relevance feedback by introducing learning to rank algorithm to re-rank expansion terms. Some term features are obtained from the original query terms and the expansion terms, learning from which we can get a new ranking list of expansion terms. Adding the expansion terms list to the original query, we can acquire more relevant documents and improve the rate of accuracy. Experimental results on the TREC dataset shows that incorporating ranking algorithms in query expansion can lead to better retrieval performance.