Cross-lingual Predicate Cluster Acquisition to Improve Bilingual Event Extraction by Inductive Learning

Abstract

In this paper we present two approaches to automatically extract cross-lingual predicate clusters, based on bilingual parallel corpora and cross-lingual information extraction. We demonstrate how these clusters can be used to improve the NIST Automatic Content Extraction (ACE) event extraction task1. We propose a new inductive learning framework to automatically augment background data for low-confidence events and then conduct global inference. Without using any additional data or accessing the baseline algorithms this approach obtained significant improvement over a state-of-the-art bilingual (English and Chinese) event extraction system.