A data-centric way to improve entity linking in knowledge-based question answering

ABSTRACT: Entity linking in knowledge-based question answering (KBQA) is intended to construct a mapping relation between a mention in a natural language question and an entity in the knowledge base. Most research in entity linking focuses on long text, but entity linking in open domain KBQA is more concerned with short text. Many recent models have tried to extract the features of raw data by adjusting the neural network structure. However, the models only perform well with several datasets. We therefore concentrate on the data rather than the model itself and created a model DME (Domain information Mining and Explicit expressing) to extract domain information from short text and append it to the data. The entity linking model will be enhanced by training with DME-processed data. Besides, we also d

SUBMITTER: Liu S 

PROVIDER: S-EPMC10280402 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature