Using Cross-Entity Inference to Improve Event Extraction

Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, pages 1127–1136, Portland, Oregon, June 19-24, 2011. c⃝2011 Association for Computational Linguistics Using Cross-Entity Inference to Improve Event Extraction Yu Hong Jianfeng Zhang Bin Ma Jianmin Yao Guodong Zhou Qiaoming Zhu School of Computer Science and Technology, Soochow University, Suzhou City, China {hongy, jfzhang, bma, jyao, gdzhou, qmzhu}@suda.edu.cn Abstract Event extraction is the task of detecting certain specified types of events that are mentioned in the source language data. The state-of-the-art research on the task is transductive inference (e.g. cross-event inference). In this paper, we propose a new method of event extraction by well using cross-entity inference. In contrast to previous inference methods, we regard entity- type consistency as key feature to predict event mentions. We adopt this inference method to improve the traditional sentence-level event ex- traction system. Experiments show that we can get 8.6% gain in trigger (event) identification, and more than 11.8% gain for argument (role) classification in ACE event extraction. 1 Introduction The event extraction task in ACE (Automatic Con- tent Extraction) evaluation involves three challeng- ing issues: distinguishing events of different types, finding the participants of an event and determin- ing the roles of the participants. The recent researches on the task show the availability of transductive inference, such as that of the following methods: cross-document, cross- sentence and cross-event inferences. Transductive inference is a process to use the known instances to predict the attributes of unknown instances. As an example, given a target event, the cross-event in- ference can predict its type by well using the re- lated events co-occurred with it within the same document. From the sentence: (1)He left the company. it is hard to tell whether it is a Transport event in ACE, which means that he left the place; or an End-Position event, which means that he retired from the company. But cross-event inference can use a related event “Then he went shopping” within the same document to identify it as a Transport event correctly. As the above example might suggest, the avail- ability of transductive inference for event extrac- tion relies heavily on the known evidences of an event occurrence in specific condition. However, the evidence supporting the inference is normally unclear or absent. For instance, the relation among events is the key clue for cross-event inference to predict a target event type, as shown in the infer- ence process of the sentence (1). But event relation extraction itself is a hard task in Information Ex- traction. So cross-event inference often suffers from some false evidence (viz., misleading by un- related events) or lack of valid evidence (viz., un- successfully extracting related events). In this paper, we propose a new method of transductive inference, named cross-entity infer- ence, for event extraction by well using the rela- tions among entities. This method is firstly motivated by the inherent ability of entity types in revealing event types. From the sentences: (2)He left the bathroom. (3)He left Microsoft. it is …

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