Refining Event Extraction through Cross-Document Inference

We apply the hypothesis of "One Sense Per Discourse" (Yarowsky, 1995) to information extraction (IE), and extend the scope of "discourse" from one single document to a cluster of topically-related documents. We employ a similar approach to propagate consistent event arguments across sentences and documents. Combining global evidence from related documents with local decisions, we design a simple scheme to conduct cross-document inference for improving the ACE event extraction task1 . Without using any additional labeled data this new approach obtained 7.6% higher F-Measure in trigger labeling and 6% higher F-Measure in argument labeling over a state-of-the-art IE system which extracts events independently for each sentence.

• Type: Conference
• Year: 2008
• Where: ACL
• Authors: Heng Ji, Ralph Grishman
• Keywords: ACE Event Extraction, ACL 2008, Computational Linguistics, Consistent Event Arguments, Sense Per Discourse