Using search session context for named entity recognition in query

Abstract

Recently, the problem of Named Entity Recognition in Query (NERQ) is attracting increasingly attention in the field of information retrieval. However, the lack of context information in short queries makes some classical named entity recognition (NER) algorithms fail. In this paper, we propose to utilize the search session information before a query as its context to address this limitation. We propose to improve two classical NER solutions by utilizing the search session context, which are known as Conditional Random Field (CRF) based solution and Topic Model based solution respectively. In both approaches, the relationship between current focused query and previous queries in the same session are used to extract novel context aware features. Experimental results on real user search session data show that the NERQ algorithms using search session context performs significantly better than the algorithms using only information of the short queries.