关键词: 中文事件检测, 预训练模型, 条件随机场（CRF）

Abstract:

Event extraction is one of the key tasks of information extraction in natural language processing. Event detection is the first step of event extraction and aims to identify and classify trigger words in an event. The existing Chinese event detection has error transfer caused by word segmentation, which leads to inaccurate extraction of trigger words. In this paper, Chinese event detection is regarded as a sequence tagging task, and an event detection model based on pre-training model and conditional random field is proposed. Firstly, the BIO annotation method is used to annotate the data. Then, the training data are obtained through the pre-training model BERT to obtain the trigger words characteristics based on the long-distance dynamic word vector. Finally, the trigger words are classified by conditional random field. Experiments on the ACE2005 Chinese corpus show that the accuracy, recall rate and F1 value of the Chinese event detection model proposed in this paper outperform other existing event detection models.

Key words: Chinese event detection, pre-training model, Conditional Random Field（CRF）