Integrating Hybrid Features for Document-Level Event Role Extraction Method

Abstract

Event extraction is a sub-task of information extraction and is an important part of natural language processing. Depending on the range of features used, event extraction methods are classified as sentence-level or document-level. However, document-level event extraction is more practical for practical tasks. Document-level event extraction is a difficult task, as it requires features to be extracted from a larger amount of text to determine which span of text is the desired event element. However, most methods do not utilize both sentence-level and document-level features. In order to utilize hybrid feature information and fuse it, this paper proposes a document-level event extraction method that integrating hybrid features. The event extraction method is based on Dynamic Multi-Pooling Convolutional Neural Network (DMCNN) and Bi-directional Long Short-Term Memory (BiLSTM), combined with self-attention mechanisms and Conditional Random Field (CRF). We evaluate the model proposed in this paper on the MUC-4 dataset and the experimental results show that our proposed model outperforms previous work.