Abstract: Information extraction technology is used to extract the information with high attention from unstructured text data. Event extraction technology is a challenging research direction in the field of information extraction. The purpose of event extraction is to extract key elements describing events from unstructured text data and present them in a structured way. Event extraction is regarded as a sequence annotation task. Firstly, the ALBERT pre-trained model is used to learn the features. Then, conditional random field is introduced to improve the sequence annotation performance. Finally, the identification and classification of event types and event elements are completed. The experimental results on ACE2005 standard corpus show that, compared with the existing models, ALBERT-CRF model improves the recall rate and F-score in trigger word recognition and classification tasks.

Key words: event extraction, sequence labeling, ALBERT model, conditional random field model