Chinese Governmental Named Entity Recognition

Named entity recognition (NER) is a fundamental task in natural language processing and there is a lot of interest on vertical NER such as medical NER, short text NER etc. In this paper, we study the problem of Chinese governmental NER (CGNER). CGNER serves as the basis for automatic governmental text analysis, which can greatly benefit the public. Considering the characteristics of the governmental text, we first formulate the task of CGNER, adding one new entity type, i.e., policy (POL) in addition to the generic types such as person (PER), location (LOC), organization (ORG) and title (TIT) for recognition. Then we constructed a dataset called GOV for CGNER. We empirically evaluate the performances of mainstream NER tools and state-of-the-art BiLSTM-CRF method on the GOV dataset. It was found that there is a performance decline compared to applying these methods on generic NER dataset. Further studies show that compound entities account for a non-negligible proportion and using the classical BIO (Begin-Inside-Outside) annotation cannot encode the entity type combination effectively. To alleviate the problem, we propose to utilize the compound tagging and BiLSTM-CRF for doing CGNER. Experiments show that our proposed methods can significantly improve the CGNER performance, especially for the LOC, ORG and TIT entity types.