计算机科学 ›› 2022, Vol. 49 ›› Issue (6): 319-325.doi: 10.11896/jsjkx.210600123
赵丹丹1,2, 黄德根1, 孟佳娜2, 董宇2, 张攀2
ZHAO Dan-dan1,2, HUANG De-gen1, MENG Jia-na2, DONG Yu2, ZHANG Pan2
摘要： 实体关系分类作为自然语言处理的基础任务,对知识图谱、智能问答、语义网构建等任务都起到了非常关键的作用。文中构建了BERT-GRU-ATT模型,以进行中文实体关系分类。为消除中文分词歧义对实体关系分类的影响,引入预训练模型BERT作为嵌入层,以较好地获得汉字的上下文信息;再通过双向门控循环单元捕获实体在句子中的长距离依赖,通过自注意力机制加强对关系分类贡献明显的字的权重,从而获得较好的实体关系分类结果。为了丰富中文实体关系分类语料,将SemEval2010_Task8英文实体关系评测语料翻译为中文1),该模型在此翻译语料上取得了75.46%的F1值,说明了所提模型的有效性。此外,所提模型在SemEval2010-task8英文数据集上F1值达到了80.55%,证明该模型对英文语料具有一定的泛化能力。
中图分类号:
| [1] XU J,ZHANG Z X,WU Z X.Review on Techniques of Entity Relation Extraction[J].Data Analysis and Knowledge Discovery,2008,24(8):18-23. [2] LIU Q,LI Y,DUAN H,et al.Knowledge Graph ConstructionTechniques[J].Journal of Computer Research and Development,2016,53(3):582-600. [3] LIU Y J.Research on Automatic Extraction of Chinese NamedEntities and Entity Relations[D].Zhengzhou:Zhengzhou University,2019. [4] E H H,ZHANG W J,XIAO S Q,et al.Survey of Entity Relationship Extraction Based on Deep Learning[J].Journal of Software,2019,30(6):1793-1818. [5] LI D M,ZHANG Y,LI D Y,et al.Review of Entity Relation Extraction Methods[J].Journal of Computer Research and Development,2020,57(7):1424-1448. [6] DENG B,FAN X Z,YANG L G.Entity Relation ExtractionMethod Using Semantic Pattern[J].Computer Engineering,2007,33(10):212-214. [7] CHE W X,LIU T,LI S.Automatic Entity Relation Extraction[J].Journal of Chinese Information Processing,2005,19(2):2-7. [8] GUO X Y,HE T T,HU X H,et al.Chinese Named Entity Relation Extraction Based on Syntactic and Semantic Features[J].Journal of Chinese Information Processing,2014,28(6):183-189. [9] GAO J P,ZHANG H,ZHAO X J,et al.Evolutionary Relation Extraction for Domain Knowledge in Wikipedia[J].Chinese Journal of Computers,2016,39(10):2088-2101. [10] LIU K B,LI F,LIU L,et al.Implementation of a Kernel-Based Chinese Relation Extraction System[J].Journal of Computer Research and Development,2007,44(8):1406-1411. [11] GUO J Y,CHEN P,YU Z T,et al.Domain Specific Chinese Semantic Relation Extraction Based on Composite Kernel[J].Journal of Chinese Information Processing,2016,30(1):24-30. [12] YU H H,QIAN L H,ZHOU G D,et al.Chinese Semantic Rela-tion Extraction Based on Unified Syntactic and Entity Semantic Tree[J].Journal of Chinese Information Processing,2010,24(5):7-23. [13] PETRONI F,CORRO L,GEMULLA R.Core:Context-awareopen relation extraction with factorization machines[C]//Proceedings of the 2015 Conference on Empirical Methodes in Na-tural Language Processing.Lisbon,Portugal,2015:1763-1773. [14] QIU L K,ZANG Y.ZORE:A syntax-based system for Chinese open relation extraction[C]//Proceedings of the 2014 Confe-rence on Empirical Methods in Natural Language Processing.Doha,Qatar,2014:1870-1880. [15] QIN B,LIU A A,LIU T.Unsupervised Chinese Open EntityRelation Extraction[J].Journal of Computer Research and Development,2015,52(5):1029-1035. [16] ZENG D,LIU K,LAI S,et al.Relation classification via convolutional deep neural network[C]//Proceedings of the 25th International Conference on Computational Linguistics:Technical Papers(COLING 2014).Dublin,Ireland,2014:2335-2344. [17] XU K,FENG Y,HUANG S,et al.Semantic relation classification via convolutional neural networks with simple negative sampling[C]//Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing.Lisbon,Portugal,2015:941-949. [18] SANTOS C N D,XIANG B,ZHOU B.Classifying relations by ranking with convolutional neural networks[C]//Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing.Beijing,China,2015:132-137. [19] YE H,CHAO W H,LUO Z C,et al.Jointly extracting relations with class ties via effective deep ranking[C]//Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics.Vancouver,Canada,2017:1810-1820. [20] BAI F,RITTER A.Structured minimally supervised learning for neural relation extraction[C]//Proceedings of the 2019 Confe-rence of the North American Chapter of the Association for Computational Linguistics.Minneapolis,Minnesota,2019:3057-3069. [21] GAO D,PENG D L,LIU C.Entity Relation Extraction Based on CNN in Large-scale Text Data[J].Journal of Chinese Computer Systems 2018,39(5):1021-1026. [22] SUN J D,GU X S,LI Y,et al.Chinese entity relation extraction algorithms based on COAE2016 datasets[J].Journal of Shandong University(Natural Science) 2017,52(9):7-12,18. [23] SOCHER R,HUVAL B,MANNING C D,et al.Semantic Compositionality through Recursive Mtrix-Vector Spaces[C]//Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Prcessing and Computational Natural Language Learning.Stroudsburg,PA,UA,2012:1201-1211. [24] LIN Y K,SHEN S Q,LIU Z Y,et al.Neural relation extraction with selective attention over instances[C]//Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics.Berlin,Germany,2016:2124-2133. [25] CHEN L,TIMOTHY M,DMITRIY D,et al.Self-training im-proves recurrent neural networks performance for temporal relation extraction[C]//Proceedings of the 9th International Workshop on Health Text Mining and Information Analysis.Brussels,Belgium,2018:165-176. [26] XU Y,MOU L L,LI G,et al.Classifying relations via long short term memory networks along shortest dependency paths[C]//Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing.Lisbon,Portugal,2015:1785-1794. [27] NGOC T V,ADEL H,GUPTA P,et al.Combining recurrentand convolutional neural networks for relation classification[C]//Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics.San Diego,California,2016:534-539. [28] SCHLICHTKRULL M S,KIPF T,BLOEM P,et al.Modeling relational data with graph convolutional networks[C]//Proceedings of the Europen semantic Web Conference on.Heraklion,Crete,Greece,2018:593-607. [29] ZHANG Y H,QI P,MANNING C D.Graph convolution over pruned dependency trees improves relation extraction[C]//Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing.Brussels,Belgium,2018:2205-2215. [30] SONG L F,ZHANG Y,WANG Z G,et al.N-ary relation extraction using graph state LSTM[C]//Proceedings of the 2018 Conference on Empirical Methods in Natural Language Proces-sing.Brussels,Belgium,2018:2226-2235. [31] GUO Z J,ZHANG Y,LU W.Attention guided graph convolutional networks for relation extraction[C]//Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics.Florence,Italy,2019:241-251. [32] CHEN Y,ZHENG D Q,ZHAO T J.Chinese relation extraction based on Deep Belief Nets[J].Journal of Software,2012,23(10):2572-2585. |
| [1] | 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 |
| [2] | 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 |
| [3] | 张嘉淏, 刘峰, 齐佳音. 一种基于Bottleneck Transformer的轻量级微表情识别架构 Lightweight Micro-expression Recognition Architecture Based on Bottleneck Transformer 计算机科学, 2022, 49(6A): 370-377. https://doi.org/10.11896/jsjkx.210500023 |
| [4] | 刘硕, 王庚润, 彭建华, 李柯. 基于混合字词特征的中文短文本分类算法 Chinese Short Text Classification Algorithm Based on Hybrid Features of Characters and Words 计算机科学, 2022, 49(4): 282-287. https://doi.org/10.11896/jsjkx.210200027 |
| [5] | 胡艳丽, 童谭骞, 张啸宇, 彭娟. 融入自注意力机制的深度学习情感分析方法 Self-attention-based BGRU and CNN for Sentiment Analysis 计算机科学, 2022, 49(1): 252-258. https://doi.org/10.11896/jsjkx.210600063 |
| [6] | 侯宏旭, 孙硕, 乌尼尔. 蒙汉神经机器翻译研究综述 Survey of Mongolian-Chinese Neural Machine Translation 计算机科学, 2022, 49(1): 31-40. https://doi.org/10.11896/jsjkx.210900006 |
| [7] | 汤世征, 张岩峰. DragDL:一种易用的深度学习模型可视化构建系统 DragDL:An Easy-to-Use Graphical DL Model Construction System 计算机科学, 2021, 48(8): 220-225. https://doi.org/10.11896/jsjkx.200900045 |
| [8] | 刘文洋, 郭延哺, 李维华. 识别关键蛋白质的混合深度学习模型 Identifying Essential Proteins by Hybrid Deep Learning Model 计算机科学, 2021, 48(8): 240-245. https://doi.org/10.11896/jsjkx.200700076 |
| [9] | 王胜, 张仰森, 陈若愚, 向尕. 基于细粒度差异特征的文本匹配方法 Text Matching Method Based on Fine-grained Difference Features 计算机科学, 2021, 48(8): 60-65. https://doi.org/10.11896/jsjkx.200700008 |
| [10] | 徐少伟, 秦品乐, 曾建朝, 赵致楷, 高媛, 王丽芳. 基于多级特征和全局上下文的纵膈淋巴结分割算法 Mediastinal Lymph Node Segmentation Algorithm Based on Multi-level Features and Global Context 计算机科学, 2021, 48(6A): 95-100. https://doi.org/10.11896/jsjkx.200700067 |
| [11] | 王习, 张凯, 李军辉, 孔芳, 张熠天. 联合自注意力和循环网络的图像标题生成 Generation of Image Caption of Joint Self-attention and Recurrent Neural Network 计算机科学, 2021, 48(4): 157-163. https://doi.org/10.11896/jsjkx.200300146 |
| [12] | 黄欣, 雷刚, 曹远龙, 陆明名. 基于深度学习的交互式问答研究综述 Review on Interactive Question Answering Techniques Based on Deep Learning 计算机科学, 2021, 48(12): 286-296. https://doi.org/10.11896/jsjkx.210100209 |
| [13] | 后同佳, 周良. 基于双向GRU神经网络和注意力机制的中文船舶故障关系抽取方法 Chinese Ship Fault Relation Extraction Method Based on Bidirectional GRU Neural Network and Attention Mechanism 计算机科学, 2021, 48(11A): 154-158. https://doi.org/10.11896/jsjkx.210100215 |
| [14] | 周小诗, 张梓葳, 文娟. 基于神经网络机器翻译的自然语言信息隐藏 Natural Language Steganography Based on Neural Machine Translation 计算机科学, 2021, 48(11A): 557-564. https://doi.org/10.11896/jsjkx.210100015 |
| [15] | 李杭, 李维华, 陈伟, 杨仙明, 曾程. 基于Node2vec和知识注意力机制的诊断预测 Diagnostic Prediction Based on Node2vec and Knowledge Attention Mechanisms 计算机科学, 2021, 48(11A): 630-637. https://doi.org/10.11896/jsjkx.210300070 |
|
||
首页