Type-enriched Hierarchical Contrastive Strategy for Fine-Grained Entity Typing
This addresses the challenge of distinguishing multi-grained similar types in entity typing for natural language processing applications, representing an incremental advance.
The paper tackles the problem of fine-grained entity typing by directly modeling differences between hierarchical types, achieving significant performance improvements on three benchmarks (BBN, OntoNotes, and FIGER).
Fine-grained entity typing (FET) aims to deduce specific semantic types of the entity mentions in text. Modern methods for FET mainly focus on learning what a certain type looks like. And few works directly model the type differences, that is, let models know the extent that one type is different from others. To alleviate this problem, we propose a type-enriched hierarchical contrastive strategy for FET. Our method can directly model the differences between hierarchical types and improve the ability to distinguish multi-grained similar types. On the one hand, we embed type into entity contexts to make type information directly perceptible. On the other hand, we design a constrained contrastive strategy on the hierarchical structure to directly model the type differences, which can simultaneously perceive the distinguishability between types at different granularity. Experimental results on three benchmarks, BBN, OntoNotes, and FIGER show that our method achieves significant performance on FET by effectively modeling type differences.