Prototypical Representation Learning for Relation Extraction

Understand

Recognizing relations between entities is a pivotal task of relational learning.

• Learning relation representations from distantly-labeled datasets is difficult because of the abundant label noise and complicated expressions in human language.
• This paper aims to learn predictive, interpretable, and robust relation representations from distantly-labeled data that are effective in different settings, including supervised, distantly supervised, and few-shot learning.
• Instead of solely relying on the supervision from noisy labels, we propose to learn prototypes for each relation from contextual information to best explore the intrinsic semantics of relations.