Joint Learning Templates and Slots for Event Schema Induction

TL;DR

This paper introduces a joint entity-driven model for automatic event schema induction that simultaneously learns event templates and slots by leveraging sentence constraints and entity semantics, improving clustering accuracy.

Contribution

It presents a novel joint learning approach that integrates template and slot induction with entity semantics and normalized cut criteria, enhancing event schema extraction.

Findings

Achieves higher accuracy than previous methods

Effectively clusters templates and slots using sentence constraints

Utilizes entity semantics for improved connectivity

Abstract

Automatic event schema induction (AESI) means to extract meta-event from raw text, in other words, to find out what types (templates) of event may exist in the raw text and what roles (slots) may exist in each event type. In this paper, we propose a joint entity-driven model to learn templates and slots simultaneously based on the constraints of templates and slots in the same sentence. In addition, the entities' semantic information is also considered for the inner connectivity of the entities. We borrow the normalized cut criteria in image segmentation to divide the entities into more accurate template clusters and slot clusters. The experiment shows that our model gains a relatively higher result than previous work.