南京理工大学学报（自然科学版）2023，Vol.47Issue(6)：748-755,8.DOI:10.14177/j.cnki.32-1397n.2023.47.06.003
基于预训练语言模型与多任务学习的事件检测方法
Event detection method based on pre-trained language models with multi-task learning
摘要
Abstract
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.关键词
词义消歧/预训练模型/多任务学习/事件检测/语料稀疏/触发词识别/条件随机场/触发词分类Key words
word sense disambiguation/pre-training model/multi-task learning/event detection/sparse corpus/trigger word recognition/conditional random field/trigger word classification分类
信息技术与安全科学引用本文复制引用
韩如雪,杨苗,宫小泽,胡镑,王永利,熊伟,赵显伟,徐琳..基于预训练语言模型与多任务学习的事件检测方法[J].南京理工大学学报（自然科学版）,2023,47(6):748-755,8.基金项目
国家自然科学基金(61941113) （61941113）
信息系统工程重点实验室开放基金(05202004 （）
05202104) （）
