基于头实体注意力的实体关系联合抽取方法

doi:10.11772/j.issn.1001-9081.2020111842

计算机应用 ›› 2021, Vol. 41 ›› Issue (9): 2517-2522.DOI: 10.11772/j.issn.1001-9081.2020111842

所属专题：人工智能

基于头实体注意力的实体关系联合抽取方法

刘雅璇^1,2, 钟勇^1,2

1. 中国科学院成都计算机应用研究所, 成都 610041;
2. 中国科学院大学, 北京 100049

收稿日期:2020-11-24 修回日期:2021-03-24 出版日期:2021-09-10 发布日期:2021-05-12
通讯作者: 钟勇
作者简介:刘雅璇(1997-),女,江西上饶人,硕士研究生,主要研究方向:自然语言处理、大数据;钟勇(1966-),男,四川岳池人,研究员,博士,CCF会员,主要研究方向:大数据、人工智能、软件过程。
基金资助:
四川省科技成果转移转化平台项目（2020ZHCG0002）。

Joint extraction method of entities and relations based on subject attention

LIU Yaxuan^1,2, ZHONG Yong^1,2

1. Chengdu Institute of Computer Application, Chinese Academy of Sciences, Chengdu Sichuan 610041, China;
2. University of Chinese Academy of Sciences, Beijing 100049, China

Received:2020-11-24 Revised:2021-03-24 Online:2021-09-10 Published:2021-05-12
Supported by:
This work is partially supported by the Science and Technology Achievement Transformation Platform Program of Sichuan Province (2020ZHCG0002).

摘要/Abstract

摘要： 实体关系抽取是构建大规模知识图谱及各种信息抽取任务的关键步骤。基于预训练语言模型，提出基于头实体注意力的实体关系联合抽取方法。该方法采用卷积神经网络（CNN）提取头实体关键信息，并采用注意力机制捕获头实体与尾实体之间的依赖关系，构建了基于头实体注意力的联合抽取模型（JSA）。在公共数据集纽约时报语料库（NYT）和采用远程监督方法构建的人工智能领域数据集上进行实验，所提模型的F1值相较于级联二元标记框架（CasRel）分别获得了1.8和8.9个百分点的提升。

关键词: 实体关系抽取, 联合抽取, 自然语言处理, 注意力机制, 领域知识图谱, 头实体

Abstract: Extracting entities and relations is crucial for building large-scale knowledge graph and different knowledge extraction tasks. Based on the pre-trained language model, an entity-oriented joint extraction method combining subject attention was proposed. In this method, the key information of the subject was extracted by using Convolutional Neural Network (CNN) and the dependency relationship between the subject and the object was captured by the attention mechanism. Followed by the above, a Joint extraction model based on Subject Attention (JSA) was built. In experiments on public dataset New York Times corpus (NYT) and the dataset of artificial intelligence built by distant supervision, the F1 score of the proposed model was improved by 1.8 and 8.9 percentage points respectively compared with Cascade binary tagging framework for Relational triple extraction (CasRel).

Key words: entity and relation extraction, joint extraction, Natural Language Processing (NLP), attention mechanism, domain knowledge graph, subject

中图分类号:

TP182

刘雅璇, 钟勇. 基于头实体注意力的实体关系联合抽取方法[J]. 计算机应用, 2021, 41(9): 2517-2522.

LIU Yaxuan, ZHONG Yong. Joint extraction method of entities and relations based on subject attention[J]. Journal of Computer Applications, 2021, 41(9): 2517-2522.

参考文献

[1] 马娜梅. 大数据背景下图书馆知识咨询服务策略[J]. 图书馆研究,2019,44(4):90-93.(MA N M. The service strategy of knowledge consultation in libraries under the background of big data[J]. Library Research,2019,44(4):90-93.)
[2] DOS SANTOS C,XIANG B,ZHOU B W. Classifying relations by ranking with convolutional neural networks[C]//Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics/the 7th International Joint Conference on Natural Language Processing. Stroudsburg, PA:Association for Computational Linguistics,2015:626-634.
[3] HASHIMOTO K, MIWA M, TSURUOKA Y, et al. Simple customization of recursive neural networks for semantic relation classification[C]//Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA:Association for Computational Linguistics,2013:1372-1376.
[4] NGUYEN T H,GRISHMAN R. Combining neural networks and log-linear models to improve relation extraction[EB/OL]. (2015-11-18)[2020-08-20]. https://arxiv.org/pdf/1511.05926.pdf.
[5] DEVLIN J,CHANG M W,LEE K,et al. BERT:pre-training of deep bidirectional transformers for language understanding[C]//Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies. Stroudsburg, PA:Association for Computational Linguistics,2019:4171-4186.
[6] 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. Stroudsburg, PA:Association for Computational Linguistics, 2019:241-251.
[7] FU T J,LI P H,MA W Y. GraphRel:modeling text as relational graphs for joint entity and relation extraction[C]//Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Stroudsburg, PA:Association for Computational Linguistics,2019:1409-1418.
[8] SOARES L B,FITZGERALD N,LING J,et al. Matching the blanks:Distributional similarity for relation learning[C]//Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Stroudsburg, PA:Association for Computational Linguistics,2019:2895-2905.
[9] 鄂海红, 张文静, 肖思琪, 等. 深度学习实体关系抽取研究综述[J]. 软件学报,2019,30(6):1793-1818.(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.)
[10] YUAN Y,ZHOU X F,PAN S R,et al. A relation-specific attention network for joint entity and relation extraction[C]//Proceedings of the 29th International Joint Conference on Artificial Intelligence. Palo Alto,CA:AAAI Press,2020:4054-4060.
[11] SOCHER R, HUVAL B, MANNING C D, et al. Semantic compositionality through recursive matrix-vector spaces[C]//Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning. Stroudsburg, PA:Association for Computational Linguistics,2012:1201-1211.
[12] ZENG D J,LIU K,LAI S W,et al. Relation classification via convolutional deep neural network[C]//Proceedings of the 25th International Conference on Computational Linguistics:Technical Papers. Stroudsburg, PA:Association for Computational Linguistics,2014:2335-2344.
[13] WANG L L,CAO Z,DE MELO G,et al. Relation classification via multi-level attention CNNs[C]//Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics. Stroudsburg, PA:Association for Computational Linguistics, 2016:1298-1307.
[14] 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. Stroudsburg,PA:Association for Computational Linguistics,2015:1785-1794.
[15] CAI R, ZHANG X D, WANG H F. Bidirectional recurrent convolutional neural network for relation classification[C]//Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics. Stroudsburg, PA:Association for Computational Linguistics,2016:756-765.
[16] MIWA M, BANSAL M. End-to-end relation extraction using LSTMs on sequences and tree structures[C]//Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics. Stroudsburg, PA:Association for Computational Linguistics,2016:1105-1116.
[17] ZHENG S C,WANG F,BAO H Y,et al. Joint extraction of entities and relations based on a novel tagging scheme[C]//Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics. Stroudsburg, PA:Association for Computational Linguistics,2017:1227-1236.
[18] ZHONG Z X,CHEN D Q. A frustratingly easy approach for joint entity and relation extraction[C]//Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies. Stroudsburg, PA:Association for Computational Linguistics, 2021:50-61.
[19] DAI D,XIAO X Y,LYU Y J,et al. Joint extraction of entities and overlapping relations using position-attentive sequence labeling[C]//Proceedings of the 33rd AAAI Conference on Artificial Intelligence. Palo Alto,CA:AAAI Press,2019:6300-6308.
[20] YU B W,ZHANG Z Y,SU X B. Joint extraction of entities and relations based on a novel decomposition strategy[C]//Proceedings of the 24th European Conference on Artificial Intelligence. Amsterdam:IOS Press,2020:2282-2289.
[21] WEI Z P,SU J L,WANG Y,et al. A novel cascade binary tagging framework for relational triple extraction[C]//Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Stroudsburg, PA:Association for Computational Linguistics,2020:1476-1488.

基于头实体注意力的实体关系联合抽取方法

Joint extraction method of entities and relations based on subject attention

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	谢德峰, 吉建民. 融入句法感知表示进行句法增强的语义解析[J]. 计算机应用, 2021, 41(9): 2489-2495.
[2]	代雨柔, 杨庆, 张凤荔, 周帆. 基于自监督学习的社交网络用户轨迹预测模型[J]. 计算机应用, 2021, 41(9): 2545-2551.
[3]	李康康, 张静. 基于注意力机制的多层次编码和解码的图像描述模型[J]. 计算机应用, 2021, 41(9): 2504-2509.
[4]	赵宏, 孔东一. 图像特征注意力与自适应注意力融合的图像内容中文描述[J]. 计算机应用, 2021, 41(9): 2496-2503.
[5]	党伟超, 李涛, 白尚旺, 高改梅, 刘春霞. 基于自注意力长短期记忆网络的Web软件系统实时剩余寿命预测方法[J]. 计算机应用, 2021, 41(8): 2346-2351.
[6]	周险兵, 樊小超, 任鸽, 杨勇. 基于多层次语义特征的英文作文自动评分方法[J]. 计算机应用, 2021, 41(8): 2205-2211.
[7]	李扬志, 袁家政, 刘宏哲. 基于时空注意力图卷积网络模型的人体骨架动作识别算法[J]. 计算机应用, 2021, 41(7): 1915-1921.
[8]	张洋, 江铭虎. 基于注意力机制的文本作者识别[J]. 计算机应用, 2021, 41(7): 1897-1901.
[9]	高钦泉, 黄炳城, 刘文哲, 童同. 基于改进CenterNet的竹条表面缺陷检测方法[J]. 计算机应用, 2021, 41(7): 1933-1938.
[10]	李朝, 兰海, 魏宪. 基于注意力的毫米波-激光雷达融合目标检测[J]. 计算机应用, 2021, 41(7): 2137-2144.
[11]	武维, 李泽平, 杨华蔚, 林川, 王忠德. 融合内容特征和时序信息的深度注意力视频流行度预测模型[J]. 计算机应用, 2021, 41(7): 1878-1884.
[12]	沈雪雯, 王晓东, 姚宇. 基于空间分频的超声图像分割注意力网络[J]. 计算机应用, 2021, 41(6): 1828-1835.
[13]	刘世泽, 朱奕达, 陈润泽, 罗海勇, 赵方, 孙艺, 王宝会. 基于残差时域注意力神经网络的交通模式识别算法[J]. 计算机应用, 2021, 41(6): 1557-1565.
[14]	李想, 王卫兵, 尚学达. 指针生成网络和覆盖损失优化的Transformer在生成式文本摘要领域的应用[J]. 计算机应用, 2021, 41(6): 1647-1651.
[15]	赖雪梅, 唐宏, 陈虹羽, 李珊珊. 基于注意力机制的特征融合-双向门控循环单元多模态情感分析[J]. 计算机应用, 2021, 41(5): 1268-1274.