We apply the hypothesis of “One Sense Per Discourse” (Yarowsky, 1995) to information extraction (IE), and extend the scope of “dis- course” 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 doc- uments with local decisions, we design a sim- ple scheme to conduct cross-document inference for improving the ACE event ex- traction task 1 . 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.

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