An Entity Linking Method Based on Entity Category and Word Embedding

Abstract

Named entity linking is a process of linking a given reference in a document to a knowledge base. In natural language processing, entity linking can enhance the computer’s understanding of unstructured text data. Applying traditional entity linking methods, especially entity linking methods for person names and organization names, has its limitations. Similar vocabulary as an entity to be linked is difficult to make full use of its contextual semantic information for ambiguity elimination. This paper makes full use of the entity’s category attribute and the semantic information contained in the context to design an entity linking method based on entity category and semantic word embedding. First, Training text classification model based on corpus to obtain entity attributes. Then the semantic feature is extracted by the word vector template to perform entity disambiguation through the semantic classification model. Finally, the results of the entity linking are predicted by means of model ensemble. Experiments show that the accuracy of the method after fusion on the entity linking dataset has improved.