计算机科学 ›› 2019, Vol. 46 ›› Issue (8): 244-248.doi: 10.11896/j.issn.1002-137X.2019.08.040
张义杰, 李培峰, 朱巧明
ZHANG Yi-jie, LI Pei-feng, ZHU Qiao-ming
摘要： 事件时序关系分类是事件抽取的重要后续任务。随着深度学习技术的发展,神经网络在事件时序关系分类任务中发挥着重要作用。但是,对于传统的循环神经网络或卷积神经网络而言,处理结构信息和捕获长距离依赖关系仍然是一个重大挑战。针对这个问题,文中提出了一种基于自注意力机制的事件时序关系分类模型架构,它可以直接捕获句子中任意两个词例之间的关系。将该机制与非线性网络层结合,可以使事件时序关系分类的性能得到显著提高。在TimeBank-Dense和Richer Event Description数据集上的对比实验证明:所提方法优于现有的大多数神经网络方法。
中图分类号:
| [1]LIN J,YUAN C F.Extraction and Computation of Chinese Temporal Relation[J].Journal of Chinese Information Processing,2009,23(5):62-67.(in Chinese) 林静,苑春法.汉语时间关系抽取与计算[J].中文信息学报,2009,23(5):62-67. [2]ZHONG Z M,LIU Z T,ZHOU W,et al.The Model of Event Relation Representation[J].Journal of Chinese Information Processing,2009,23(6):56-60.(in Chinese) 仲兆满,刘宗田,周文,等.事件关系表示模型[J].中文信息学报,2009,23(6):56-60. [3]WANG F E,TAN H Y,QIAN Y L.Recognition of Temporal Relation in One Sentence Based on Maximum Entropy[J].Computer Engineering,2012,38(4):37-39.(in Chinese) 王风娥,谭红叶,钱揖丽.基于最大熵的句内时间关系识别[J].计算机工程,2012,38(4):37-39. [4]MARCU D,ECHIHABI A.Anunsupervised approach to recognizing discourse relations[C]∥Proceedings of the Association for Computational Linguistics.Association for Computational Linguistics,2002:368-375. [5]MANI I,VERHAGEN M,WELLNER B,et al.Machine lear- ning of temporal relations[C]∥Proceedings of the Association for Computational Linguistics.Association for Computational Linguistics,2006:753-760. [6]CHAMBERS N,WANG S,JURAFSKY D.Classifying temporal relations between events[C]∥Proceeding of the ACL on Inte-ractive Poster and Demonstration Sessions.Association for Computational Linguistics,2007:173-176. [7]LI P F,ZHU Q M,ZHOU G D,et al.Global Inference to Chinese Temporal Relation Extraction[C]∥Proceedings of the International Conference on Computational Linguistics.2016:1451-1460. [8]CHENG F,MIYAO Y.Classifying Temporal Relations by Bidirectional LSTM over Dependency Paths[C]∥Proceedings of the Association for Computational Linguistics(Short Papers).Association for Computational Linguistics.2017:1-6. [9]MENG Y,RUMSHISKY A,ROMANOV A.Temporal Infor- mation Extraction for Question Answering Using Syntactic Dependencies in an LSTM-based Architecture[C]∥Proceedings of the Conference on Empirical Methods in Natural Language Processing.Association for Computational Linguistics,2017:887-896. [10]CHOUBEY P K,HUANG R H.A Sequential Model for Classifying Temporal Relations between Intra-Sentence Events[C]∥Proceedings of the Conference on Empirical Methods in Natural Language Processing.Association for Computational Linguistics.2017:1796-1802. [11]TOURILLE J,FERRET O,TANNIER X,et al.Neural Architecture for Temporal Relation Extraction:A Bi-LSTM Approach for Detecting Narrative Containers[C]∥Proceedings of the Association for Computational Linguistics.Association for Computational Linguistics,2017:224-230. [12]VASWANI A,SHAZEER N,PARMAR N,et al.Attentionis all you need[J].arXiv:1706.03762. [13]CHENG J P,DONG L,LAPATA M.Long Short-Term Memory-Networks for Machine Reading[J].arXiv:1601.06733. [14]LIN Z H,FENG M W,SANTOS C N,et al.A Structured Self-attentive Sentence Embedding[J].arXiv:1703.03130. [15]PAULUS R,XIONG C M,SOCHER R.A Deep Reinforced Model for Abstractive Summarization[J].arXiv:1705.04304. [16]SHEN T,ZHOU T Y,LONG G D,et al.DiSAN:Directional Self-Attention Network for RNN/CNN-free Language Understanding[J].arXiv :1709.04696. [17]DEY R,SALEMT F M.Gate-variants of Gated Recurrent Unit (GRU)neural networks[J].arXiv:1701.05923. [18]DAUPHIN Y N,FAN A,AULI M,et al.Language Modeling with Gated Convolutional Networks[J].arXiv:1612.08083. [19]MIRZA P,TONELLI S.On the contribution of word embeddings to temporal relationclassification[C]∥Proceedings of the International Conference on Computational Linguistics.2016:2818-2828. |
| [1] | 饶志双, 贾真, 张凡, 李天瑞. 基于Key-Value关联记忆网络的知识图谱问答方法 Key-Value Relational Memory Networks for Question Answering over Knowledge Graph 计算机科学, 2022, 49(9): 202-207. https://doi.org/10.11896/jsjkx.220300277 |
| [2] | 汤凌韬, 王迪, 张鲁飞, 刘盛云. 基于安全多方计算和差分隐私的联邦学习方案 Federated Learning Scheme Based on Secure Multi-party Computation and Differential Privacy 计算机科学, 2022, 49(9): 297-305. https://doi.org/10.11896/jsjkx.210800108 |
| [3] | 徐涌鑫, 赵俊峰, 王亚沙, 谢冰, 杨恺. 时序知识图谱表示学习 Temporal Knowledge Graph Representation Learning 计算机科学, 2022, 49(9): 162-171. https://doi.org/10.11896/jsjkx.220500204 |
| [4] | 王剑, 彭雨琦, 赵宇斐, 杨健. 基于深度学习的社交网络舆情信息抽取方法综述 Survey of Social Network Public Opinion Information Extraction Based on Deep Learning 计算机科学, 2022, 49(8): 279-293. https://doi.org/10.11896/jsjkx.220300099 |
| [5] | 郝志荣, 陈龙, 黄嘉成. 面向文本分类的类别区分式通用对抗攻击方法 Class Discriminative Universal Adversarial Attack for Text Classification 计算机科学, 2022, 49(8): 323-329. https://doi.org/10.11896/jsjkx.220200077 |
| [6] | 姜梦函, 李邵梅, 郑洪浩, 张建朋. 基于改进位置编码的谣言检测模型 Rumor Detection Model Based on Improved Position Embedding 计算机科学, 2022, 49(8): 330-335. https://doi.org/10.11896/jsjkx.210600046 |
| [7] | 孙奇, 吉根林, 张杰. 基于非局部注意力生成对抗网络的视频异常事件检测方法 Non-local Attention Based Generative Adversarial Network for Video Abnormal Event Detection 计算机科学, 2022, 49(8): 172-177. https://doi.org/10.11896/jsjkx.210600061 |
| [8] | 胡艳羽, 赵龙, 董祥军. 一种用于癌症分类的两阶段深度特征选择提取算法 Two-stage Deep Feature Selection Extraction Algorithm for Cancer Classification 计算机科学, 2022, 49(7): 73-78. https://doi.org/10.11896/jsjkx.210500092 |
| [9] | 程成, 降爱莲. 基于多路径特征提取的实时语义分割方法 Real-time Semantic Segmentation Method Based on Multi-path Feature Extraction 计算机科学, 2022, 49(7): 120-126. https://doi.org/10.11896/jsjkx.210500157 |
| [10] | 侯钰涛, 阿布都克力木·阿布力孜, 哈里旦木·阿布都克里木. 中文预训练模型研究进展 Advances in Chinese Pre-training Models 计算机科学, 2022, 49(7): 148-163. https://doi.org/10.11896/jsjkx.211200018 |
| [11] | 周慧, 施皓晨, 屠要峰, 黄圣君. 基于主动采样的深度鲁棒神经网络学习 Robust Deep Neural Network Learning Based on Active Sampling 计算机科学, 2022, 49(7): 164-169. https://doi.org/10.11896/jsjkx.210600044 |
| [12] | 金方焱, 王秀利. 融合RACNN和BiLSTM的金融领域事件隐式因果关系抽取 Implicit Causality Extraction of Financial Events Integrating RACNN and BiLSTM 计算机科学, 2022, 49(7): 179-186. https://doi.org/10.11896/jsjkx.210500190 |
| [13] | 苏丹宁, 曹桂涛, 王燕楠, 王宏, 任赫. 小样本雷达辐射源识别的深度学习方法综述 Survey of Deep Learning for Radar Emitter Identification Based on Small Sample 计算机科学, 2022, 49(7): 226-235. https://doi.org/10.11896/jsjkx.210600138 |
| [14] | 王君锋, 刘凡, 杨赛, 吕坦悦, 陈峙宇, 许峰. 基于多源迁移学习的大坝裂缝检测 Dam Crack Detection Based on Multi-source Transfer Learning 计算机科学, 2022, 49(6A): 319-324. https://doi.org/10.11896/jsjkx.210500124 |
| [15] | 楚玉春, 龚航, 王学芳, 刘培顺. 基于YOLOv4的目标检测知识蒸馏算法研究 Study on Knowledge Distillation of Target Detection Algorithm Based on YOLOv4 计算机科学, 2022, 49(6A): 337-344. https://doi.org/10.11896/jsjkx.210600204 |
|
||
首页