A context-aware feature representation method in fine-grained entity typing.

Fine-grained entity typing assigns fine-grained types to entities in the text, which can provide entities with rich semantic information through type information, and plays important roles in downstream tasks such as relation extraction, entity linking, and question answering systems. Since the length and position of entities in sentences are not uniform, the representation of entities in context can not be calculated. Existing fine-grained entity typing models process entity mentions and their contexts separately into individual feature representations, which separates the semantic relationship between them. This paper proposes a context-aware feature representation method in fine-grained entity typing, which places entities back into their contexts and solves the problem of computing entity feature representation when the entity length and position are not uniform. Experimental results demonstrate that this method can extract the feature representation of entities in their contexts, and significantly improve the performance of fine-grained entity typing. The Macro-F1 value of this method on the Chinese finegrained entity classification dataset CFET is improved by more than 10%. [ABSTRACT FROM AUTHOR]