DEEDP: Document-Level Event Extraction Model Incorporating Dependency Paths

Author:

Li Hui1ORCID,Zhao Xin2,Yu Lin1,Zhao Yixin1,Zhang Jie1

Affiliation:

1. School of Automation, Nanjing University of Science and Technology, Nanjing 210094, China

2. Science and Technology on Information Systems Engineering Laboratory, Nanjing 210023, China

Abstract

Document-level event extraction (DEE) aims at extracting event records from given documents. Existing DEE methods handle troublesome challenges by using multiple encoders and casting the task into a multi-step paradigm. However, most of the previous approaches ignore a missing feature by using mean pooling or max pooling operations in different encoding stages and have not explicitly modeled the interdependency features between input tokens, and thus the long-distance problem cannot be solved effectively. In this study, we propose Document-level Event Extraction Model Incorporating Dependency Paths (DEEDP), which introduces a novel multi-granularity encoder framework to tackle the aforementioned problems. Specifically, we first designed a Transformer-based encoder, Transformer-M, by adding a Syntactic Feature Attention mechanism to the Transformer, which can capture more interdependency information between input tokens and help enhance the semantics for sentence-level representations of entities. We then stacked Transformer-M and Transformer to integrate sentence-level and document-level features; we thus obtained semantic enhanced document-aware representations for each entity and model long-distance dependencies between arguments. Experimental results on the benchmarks MUC-4 and ChFinAnn demonstrate that DEEDP achieves superior performance over the baselines, proving the effectiveness of our proposed methods.

Funder

Jiangsu Province Natural Science Foundation

National Natural Science Foundation of China

Publisher

MDPI AG

Subject

Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science

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