中圖分類號：TP391
文獻標志碼：A
DOI: 10.16157/j.issn.0258-7998.234136
引用格式： 樂楊，胡軍國，李耀. 基于RoBERTa和多層次特征的中文事件抽取方法[J]. 電子技術應用，2023，49(11)：49-54.

Chinese event extraction method based on RoBERTa and multi-level feature

Abstract： To address the issues of insufficient semantic representation and incomplete feature extraction in Chinese event extraction, a method based on RoBERTa and multi-level features is proposed. Firstly, by using the pre-trained RoBERTa model, word embeddings are constructed and extended based on syntactic and semantic information of trigger words. Specifically, part-of-speech tags and trigger word embeddings are integrated into the word embeddings. Secondly, global and local features are extracted using a bi-directional long short-term memory network and convolutional neural network, respectively. The self-attention mechanism is employed to capture the relationships among different features, emphasizing the utilization of important features. Finally, a conditional random field is used to achieve BIO sequence labeling, completing the event extraction process. On the DuEE1.0 dataset, the F1 scores of trigger word extraction and event argument extraction reach 86.9% and 68.0%, respectively, which are superior to existing common event extraction models, validating the effectiveness of this method.

Key words : event extraction；RoBERTa pretrained model；multi-level feature；self-attention mechanism；sequence labeling