Fine-grained Named Entity Recognition for Multi-scenario
SHENG Jian1, XIANG Zhengpeng1, QIN Bing1, LIU Ming1, WANG Lifeng2
1.Research Center for Social Computing and Information Retrieval, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China; 2.Tencent Technology (Shenzhen) CO., Ltd. Shenzhen, Guangdong 518000, China
Abstract：Name entity recognition is a classical research issue in data mining community. To recognize the entities in multi-domain with fine-grained labels, we propose a method of utilizes web thesaurus to annotate web data automatically to acquire large-scale training corpus. To minimize the influence of the noises in training corpus, we design a two-phase entity recognition method. First, the entity’s domain label is obtained. After that, the context of each recognized entity is used to determine the fine-grained label for one entity. Experimental results demonstrate that the proposed method can obtain high accuracy on entity recognition in multiple domains.
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2019, Vol. 33 
