Open Relation and Event Type Discovery with Type Abstraction

TL;DR

This paper presents a novel method for automatic relation and event type discovery using type abstraction and co-training, enabling the system to generalize and identify new types beyond predefined ontologies.

Contribution

The paper introduces a type abstraction approach combined with a co-training framework for automatic type discovery in relation and event extraction tasks.

Findings

Type abstraction improves relation and event type discovery.

The co-training framework leverages two complementary representations.

Experimental results show consistent advantages over baselines.

Abstract

Conventional closed-world information extraction (IE) approaches rely on human ontologies to define the scope for extraction. As a result, such approaches fall short when applied to new domains. This calls for systems that can automatically infer new types from given corpora, a task which we refer to as type discovery. To tackle this problem, we introduce the idea of type abstraction, where the model is prompted to generalize and name the type. Then we use the similarity between inferred names to induce clusters. Observing that this abstraction-based representation is often complementary to the entity/trigger token representation, we set up these two representations as two views and design our model as a co-training framework. Our experiments on multiple relation extraction and event extraction datasets consistently show the advantage of our type abstraction approach. Code available at https://github.com/raspberryice/type-discovery-abs.