Fine-grained entity type classification with adaptive context

Natural language processing (NLP) is the technology that enables machine to process human language. Entity recognition is one of the most basic tasks in NLP. It aims to identify and classify the name of each object in the text. Traditional named entity recognition systems can only identify a small set of types such as person, location, organization or miscellaneous. In order to make machine exploit the meaning of the text better, it is necessary to classify the entities appearing in the text to fine-grained types. Previously reported work generally obtained the entity context information through a fixed window, so the external information for classifying the entity is not enough, which may lead to ambiguity. To solve the shortcomings of these methods, this paper presents a fine-grained entity type classification method for unstructured text based on global information and sliding window context. By combining those information with other features, we utilize a bidirectional long–short-term memory network to perform the classification work. With the proposed method, the experiment results of fine-grained entity type classification are optimized.