Cross-lingual predicate cluster acquisition to improve bilingual event extraction by inductive learning
Heng Ji · 2009
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 task. 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.