Joint SPSL and CCWR for Chinese Short Text Entity Recognition and Linking

Abstract

Entity Recognition Linking (ERL) is a basic task of Natural Language Processing (NLP), which is an extension of the Named Entity Recognition (NER) task. The purpose of the ERL is to detect the entity from a given Chinese short text, and the detected entity is linking to the corresponding entity in the given knowledge library. ERL’s task include two subtasks: Entity Recognition (ER) and Entity Link (EL). Due to the lack of rich context information in Chinese short text, the accuracy of ER is not high. In different fields, the meaning of the entity is different and the entity cannot be accurately linking. These two problems have brought a big challenge to the Chinese ERL task. In order to solve these two problems, this paper proposes based on neural network model joint semi-point semi-label (SPSL) and Combine character-based and word-based representations (CCWR) embedding. The structure of this model enhances the representation of entity features and improve the performance of ER. The structure of this model enhances the representation of contextual semantic information and improve the performance of EL. In summary, this model has a good performance in ERL. In the ccks2019 Chinese short text ERL task, the F1 value of this model can reach 0.7463.