基于二部图半监督方法的查询日志实体挖掘

基于用户查询日志的命名实体挖掘的目标是从用户查询日志中挖掘一组具有指定类别的命名实体。为解决已有用户查询日志实体挖掘研究工作中的种子实体不充分的问题,提出了一种基于二部图的半监督排序方法,利用实体之间的关系(实体共享查询模板)来改善实体排序效果。该方法首先基于候选实体和查询模板构建一个二部图,然后基于二部图将种子实体的类别相关性传播到其他候选实体,最后按照类别相关性得分对候选实体进行排序,并进一步给出方法中迭代过程的等价优化框架。实验结果表明本文提出的方法优于基准方法,具有较好的挖掘效果。

Bipartite graph based semi-supervised method for entity mining from the query log

Named entity mining from query log aims to mine a list of named entities with the specific type from the query log.A bipartite graph based semi-supervised ranking method,which leverages the relationship between the entities(i.e.entities share common templates) to help improve the ranking,was proposed to resolve the scarcity of seed entity in existing work about named entity mining from the query log.First,a bipartite graph based on the candidate entities and templates was constructed.Then,the relevance score was propagated from the seed entities to other candidate entities.Finally,the candidate entities were ranked according to the relevance score.An optimization framework for the iterative process was further developed in this ranking method.Experimental results show the effectiveness of the proposed method.