Named entity recognition in query

This paper addresses the problem of Named Entity Recognition in Query (NERQ), which involves detection of the named entity in a given query and classification of the named entity into predefined classes. NERQ is potentially useful in many applications in web search. The paper proposes taking a probabilistic approach to the task using query log data and Latent Dirichlet Allocation. We consider contexts of a named entity (i.e., the remainders of queries after the named entity is removed) as words of a document, and classes of the named entity as topics. The topic model is constructed by a novel and general learning method referred to as WS-LDA (Weakly Supervised Latent Dirichlet Allocation), which employs weakly supervised learning (rather than unsupervised learning) using partially labeled seed entities. Experimental results show that the proposed method based on WS-LDA can accurately perform NERQ, and outperform the baseline methods. Categories and Subject Descriptors H.3.3 [Informa...

• Type: Conference
• Year: 2009
• Where: SIGIR
• Authors: Jiafeng Guo, Gu Xu, Xueqi Cheng, Hang Li
• Keywords: Information Retrieval, Keywords Named Entity, Latent Dirichlet Allocation, Named Entity Recognition, SIGIR 2009