Abstract

Traditional approaches to the task of ACE event detection primarily regard multiple events in one sentence as independent ones and recognize them separately by using sentence-level information. However, events in one sentence are usually interdependent and sentence-level information is often insufficient to resolve ambiguities for some types of events. This paper proposes a novel framework dubbed as Hierarchical and Bias Tagging Networks with Gated Multi-level Attention Mechanisms (HBTNGMA) to solve the two problems simultaneously. Firstly, we propose a hierachical and bias tagging networks to detect multiple events in one sentence collectively. Then, we devise a gated multi-level attention to automatically extract and dynamically fuse the sentence-level and document-level information. The experimental results on the widely used ACE 2005 dataset show that our approach significantly outperforms other state-of-the-art methods.

Highlights

  • Event detection (ED) is a crucial subtask of event extraction, which aims to identify event triggers and classify them into specific types from texts

  • According to the task defined in Automatic Context Extraction1 (ACE), given the following sentence S1, a robust ED system should be able to recognize two events: a Die event triggered by died and an Attack event triggered by fired

  • We conduct extensive experiments on a widely used ACE 2005 dataset, and the experimental results show that our approach significantly outperforms other state-of-theart methods 3

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Summary

Introduction

Event detection (ED) is a crucial subtask of event extraction, which aims to identify event triggers and classify them into specific types from texts. According to the task defined in Automatic Context Extraction (ACE), given the following sentence S1, a robust ED system should be able to recognize two events: a Die event triggered by died and an Attack event triggered by fired. S1: In Baghdad, a cameraman died when an American tank fired on the Palestine Hotel. To this end, most methods (Ahn, 2006; Hong et al, 2011; Chen et al, 2015; Nguyen and Grishman, 2016; Liu et al, 2017) model ED as a multiclassification task and predict every word in the. We will use examples to illustrate these two problems

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