关键词: 中文突发事件, 事件抽取, 命名实体识别, 多任务学习, 点阵双向长短期记忆网络, 损失平衡

Abstract: Aiming at the problem that the Bidirectional Long Short-Term Memory network-Conditional Random Field (BiLSTM-CRF) based event extraction model can only obtain the semantic information of character granularity, and the upper limit of the model is low due to the low dimensionality of learnable features, a Chinese emergency event extraction method based on named entity recognition task feedback enhancement was proposed by taking the Chinese public emergency event data in open field as the research object, namely FeedBack-Lattice-Bidirectional Long Short-Term Memory network-Conditional Random Field (FB-Latiice-BiLSTM-CRF). Firstly, the Lattice mechanism was integrated with Bidirectional Long Short-Term Memory network (BiLSTM) as the sharing layer of the model to obtain the semantic features of words in sentences. Secondly, the named entity recognition auxiliary task was added to jointly learn and mine entity semantic information. At the same time, the output of the named entity recognition task was fed back to the input end, and the word segmentation results corresponding to the entities were extracted as the external input of the Lattice mechanism, so as to reduce the computing overhead brought by the large number of self-formed words of the mechanism and further enhance the extraction of entity semantic features. Finally, the total loss of the model was calculated by the maximum Gaussian likelihood estimation method to maximize the homoscedasticity uncertainty, so as to solve the problem of loss imbalance caused by multi-task joint learning. Experimental results show that FB-Latiice-BiLSTM-CRF has the accuracy of 81.25%, the recall of 76.50%, and the F1 value of 78.80% on the test set, which are 7.63, 4.41 and 5.95 percentage points higher than those of the benchmark model, respectively, verifying the effectiveness of the improvement performing to the benchmark model.

Key words: Chinese emergency event, event extraction, named entity recognition, multi-task learning, Lattice-Bidirectional Long Short-Term Memory network (Lattice-BiLSTM), loss balance