RBPB: Regularization-Based Pattern Balancing Method for Event Extraction

Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pages 1224–1234, Berlin, Germany, August 7-12, 2016. c⃝2016 Association for Computational Linguistics RBPB: Regularization-Based Pattern Balancing Method for Event Extraction Lei Sha1,2, Jing Liu2, Chin-Yew Lin2, Sujian Li1, Baobao Chang1, Zhifang Sui1 1 Key Laboratory of Computational Linguistics, Ministry of Education School of Electronics Engineering and Computer Science, Peking University 2Knowledge Computing Group, Microsoft Research 1{shalei, lisujian, chbb, szf}@pku.edu.cn 2{liudani,cyl}@microsoft.com Abstract Event extraction is a particularly chal- lenging information extraction task, which intends to identify and classify event triggers and arguments from raw text. In recent works, when determining event types (trigger classification), most of the works are either pattern-only or feature-only. However, although patterns cannot cover all representations of an event, it is still a very important feature. In addition, when identifying and classifying arguments, previous works consider each candidate argument separately while ignoring the relationship between arguments. This paper proposes a Regularization-Based Pattern Balancing Method (RBPB). Inspired by the progress in representation learning, we use trigger embedding, sentence-level embedding and pattern features together as our features for trigger classification so that the effect of patterns and other useful features can be balanced. In addition, RBPB uses a regularization method to take advantage of the relationship between arguments. Experiments show that we achieve results better than current state-of-art equivalents. 1 Introduction Event extraction has become a popular research topic in the area of information extraction. ACE 2005 defines event extraction task1 as three sub-tasks: identifying the trigger of an event, identifying the arguments of the event, and distinguishing their corresponding roles. As an example in Figure 1, there is an “Attack” event 1http://www.itl.nist.gov/iad/mig/tests/ace/2005/ triggered by “tear through” with three arguments. Each argument has one role. In the trigger classification stage, some previous approaches (Grishman et al., 2005; Ji and Grishman, 2008; Liao and Grishman, 2010; Huang and Riloff, 2012) use patterns to decide the types of event triggers. However, pattern-based approaches suffer from low recall since real world events usually have a large variety of representations. Some other approaches (Hong et al., 2011; Li et al., 2013; Lu and Roth, 2012) identify and classify event triggers using a large set of features without using patterns. Although these features can be very helpful, patterns are still indispensable in many cases because they can identify a trigger with the correct event type with more than 96% accuracy according to our data analysis on ACE 2005 data sets. In argument identification and classification, most approaches identify each candidate argument separately without considering the relation between arguments. We define two kinds of argument relations here: (1) Positive correlation: if one candidate argument belongs to one event, then the other is more likely to belong to the same event. For example, in Figure 1, the entity “a waiting shed” shares a common dependency head “tore” with “a powerful bomb”, so when the latter entity is identified as an argument, the former is more likely to be identified. (2) Negative correlation: if one …

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