Abstract: As an important supporting technology for applications such as knowledge base construction and information retrieval, entity disambiguation plays an important role in the field of Natural Language Processing (NLP). However, in the short text environment, it is difficult for entity disambiguation to extract sufficient context features for disambiguation. Aiming at the characteristics of short texts, this paper proposes a disambiguation method of graph models based on entity topic relations. This method uses TextRank algorithm to infer the topic of corpus constructed by knowledge base information, and uses the result of topic inference as the representation of relationship between entities. By combining the disambiguation score given by the semantic matching model based on BERT, the disambiguation network graph is constructed, and the final disambiguation result is obtained through search and sorting. The data set provided in the short text entity link task of CCKS2020 is used to evaluate the method. The experimental results show that the proposed method is better than other entity linking methods in entity disambiguation of short text, and can effectively solve the entity disambiguation problem of Chinese short text.

Key words: entity disambiguation, graph model, topic inference, TextRank