Abstract: Event extraction is one of the basic tasks in the information extraction’s field， which is aims to extract structured information from unstructured text. The majority of the existing event extraction methods which are based on machine reading comprehension model directly detect and classify the input text trigger words， and to some extent ignore the prediction error caused by judging whether the input text is an event. Therefore， this paper proposes an event extraction method based on BERT-BiLSTM-Attention hybrid model. This method takes BERT-based machine reading comprehension model as the basic model， adopts multi-round question-and-answer method， and adds event classification detection module on the basis of existing machine reading comprehension model to reduce prediction error. BiLSTM model is combined with attention mechanism to form historical session information module to more effectively filter out important information and integrate it into a reading comprehension model. The event extraction experiments are conducted on ACE2005， and the results show that the accuracy， recall and F1 value are improved by 7.8 percentage points， 4.6 percentage points and 5.4 percentage points， respectively， compared with the basic model， which has certain advantages.

Key words: event extraction, machine reading comprehension, event classification, BiLSTM, attention mechanism