Enhanced Entity Mention Recognition and Disambiguation Technologies for Chinese Knowledge Base Q&A

Gang Wu, Wenfang Wu, Hangxu Ji, Xianxian Hou, Li Xia

Abstract: Entity linking, which usually involves mention recognition and entity disambiguation, is an important task in knowledge base question and answer (KBQA). However, due to the diversity of Chinese grammatical structure, the complexity of Chinese natural language expressions and the lack of contextual information, there are still many challenges in the task of the Chinese KBQA. We discussed two subtasks of the entity linking separately. For the mention recognition part, in order to get the only topic entity mention of the question, we proposed a topic entity mention recognition algorithm based on sequence annotation. The algorithm combines a variety of feature vectors based on word embedding, and uses model BiGRU-CRF model to perform sequence labeling modeling. We also proposed an entity disambiguation algorithm based on a similarity calculation with extended information. The algorithm not only realized the information expansion by crawling the candidate entity for related problems, but also made full use of contextual information by combining lexical level similarity and sentence semantic similarity. In addition, the experimental results show that the proposed entity linking solution possesses huge advantages compared to several baseline systems.