为了解决现有事件检测方法存在语料稀疏和触发词一词多义导致的触发词抽取不准确以及类型判断错误等问题,该文将双向Transformer编码表示(BERT)的预训练模型与条件随机场(CRF)结合,并联合多任务学习,提出了一种基于BERT-CRF模型与多任务学习的事件检测方法(MBCED).该方法同时进行事件检测任务和词义消歧任务,将词义消歧任务中学习到的知识转移到事件检测任务中,既补充了语料,也缓解了一词多义所导致的触发词分类不准确问题.在ACE2005 数据集上的传统事件检测模型对比实验结果表明,与动态多池卷积神经网络(DMCNN)、基于循环神经网络的联合模型(JRNN)、基于双向长短期记忆和条件随机场(BiLSTM-CRF)的联合模型、BERT-CRF方法相比,MBCED方法触发词识别的F值提升了1.2%.多任务学习模型对比实验结果表明,与基于多任务深度学习的实体与事件联合抽取(MDL-J3E)模型、基于共享BERT的多任务学习(MSBERT)模型、基于CRF多任务学习的事件抽取模型(MTL-CRF)相比,MBCED在触发词识别和触发词分类2 个子任务上的准确率都较好.
In order to solve the problems of inaccurate trigger word extraction and type judgment errors caused by sparse corpus and polysemy of trigger words in existing event detection methods,bidirectional encoder representations from Transformers(BERT)is combined with conditional random field(CRF),jointed multi-task learning,a multi-task learning event detection method(MBCED)is proposed.Event detection tasks and word sense disambiguation tasks are performed at the same time,and the knowledge learned in the word sense disambiguation task is transferred to the event detection task,which not only supplements the corpus,but also alleviates the problem of inaccurate trigger word classification caused by polysemy.The experimental results of comparing traditional event detection models on the ACE2005 dataset show that compared with dynamic multi-pooling convolutional neural networks(DMCNN),joint event extraction via recurrent neural networks(JRNN),bidirectional long and short-term memory and conditional random fields(BiLSTM-CRF),and BERT-CRF methods,the MBCED method has a 1.2%increase in F-value for triggering word recognition.The comparative experimental results of multi-task learning models show that compared with multi-task deep learning for joint extraction of entity and event(MDL-J3E),multi-task learning on shareable BERT(MSBERT),multi-task learning with CRF for event extraction model(MTL-CRF),MBCED has better accuracy in both trigger word recognition and trigger word classification subtasks.