基于转移学习的命名实体挖掘技术

A Named Entity Mining Method Based on Transfer Learning

研究了针对大规模查询日志中丰富的命名实体的挖掘技术,通过利用Wikipedia数据,结合转移学习方法构建目标类别的分类器.该技术很好地利用了监督学习的优越性能以提高查询日志中命名实体挖掘的准确性,同时也解决了监督学习方法中大规模标注的问题.实验结果表明,基于转移学习的命名实体挖掘技术具有优越的命名实体挖掘性能.

This paper addresses the problem of mining named entities from query logs.A novel scheme was introduced based on transfer learning,which trains classifier for target category by leveraging Wikipedia data source.In this way it can greatly make use of supervised learning and also deal with the large scale（labeling） problem.The experiment results show the effectiveness of the novel scheme based on transfer learning.