Refining Event Extraction through Cross-Document Inference

Proceedings of ACL-08: HLT, pages 254–262, Columbus, Ohio, USA, June 2008. c⃝2008 Association for Computational Linguistics Refining Event Extraction through Cross-document Inference Heng Ji Computer Science Department Ralph Grishman New York University New York, NY 10003, USA (hengji, grishman)@cs.nyu.edu Abstract 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 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. 1 Introduction Identifying events of a particular type within indi- vidual documents – ‘classical’ information extrac- tion – remains a difficult task. Recognizing the different forms in which an event may be ex- pressed, distinguishing events of different types, and finding the arguments of an event are all chal- lenging tasks. Fortunately, many of these events will be re- ported multiple times, in different forms, both within the same document and within topically- related documents (i.e. a collection of documents sharing participants in potential events). We can 1 http://www.nist.gov/speech/tests/ace/ take advantage of these alternate descriptions to improve event extraction in the original document, by favoring consistency of interpretation across sentences and documents. Several recent studies involving specific event types have stressed the benefits of going beyond traditional single- document extraction; in particular, Yangarber (2006) has emphasized this potential in his work on medical information extraction. In this paper we demonstrate that appreciable improvements are possible over the variety of event types in the ACE (Automatic Content Extraction) evaluation through the use of cross-sentence and cross-document evi- dence. As we shall describe below, we can make use of consistency at several levels: consistency of word sense across different instances of the same word in related documents, and consistency of argu- ments and roles across different mentions of the same or related events. Such methods allow us to build dynamic background knowledge as required to interpret a document and can compensate for the limited annotated training data which can be pro- vided for each event type. 2 Task and Baseline System 2.1 ACE Event Extraction Task The event extraction task we are addressing is that of the Automatic Content Extraction (ACE) evalu- ations2. ACE defines the following terminology: 2 In this paper we don’t consider event mention coreference resolution and so don’t distinguish event mentions and events. 254 entity: an object or a set of objects in one of the semantic categories of interest mention: a reference to an entity (typically, a noun phrase) event trigger: the main word which most clearly expresses an event occurrence event arguments: the mentions that are in- …

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