Abstract: Event detection is an important task in information extraction field,which aims to identify trigger words in raw text and then classify them into correct event types.Neural network based methods usually regard event detection as a word-wise classification task,which suffers from the mismatch problem between words and triggers when applied to Chinese.Besides,due to the multiple word senses of a trigger word,the same trigger word in different sentences causes the ambiguity problem.To address the two problems in Chinese event detection,we propose a Chinese event detection model with hierarchical and multi-granularity semantic fusion.First,we adopt a character-based sequence labelling method to solve the mismatch problem,in which we devise a Character-Word Fusion Gate to capture the semantic information of words in different segmentation ways.Then we device a Character-Sentence Fusion Gate to learn a character-word-sentence hybrid representation of sequence,which takes the semantic information of the entire sentence into condition and solves the ambiguity problem.Finally,in order to balance the influence the label “O” and the other labels,a loss function with bias is applied to train our model.The experimental results on the widely used ACE2005 dataset show that our approach outperforms at least 3.9%,1.4% and 2.9% than other Chinese event detection models under the metrics of accuracy (Precision,P),recall (Recall,R) and F1.

Key words: Bidirectional long short-term memory model, Chinese event detection, Con-volutional neural network, Information extraction, Multi-granularity semantic fusion, Pre-trained language model