基于主体掩码的实体关系抽取方法

郑慎鹏; 陈晓军; 向阳; 沈汝超

doi:10.11959/j.issn.2096-0271.2021022

您当前的位置：

首页 >

文章列表页 >

基于主体掩码的实体关系抽取方法

专题：基于大数据的知识图谱及其应用 | 更新时间：2024-06-03

- 基于主体掩码的实体关系抽取方法
- An entity relation extraction method based on subject mask
- 大数据 2021年7卷第3期页码：2021022
- 作者机构：
  
  1. 同济大学电子与信息工程学院，上海 201804
  2. 上海国际港务（集团）股份有限公司，上海 200080
- 作者简介：
  
  [ "郑慎鹏（1995- ），男，同济大学电子与信息工程学院硕士生，主要研究方向为自然语言处理。" ]
  [ "陈晓军（1995- ），男，同济大学电子与信息工程学院博士生，主要研究方向为自然语言处理。" ]
  [ "向阳（1962- ），男，同济大学电子与信息工程学院教授，主要研究方向为数据挖掘、自然语言处理、智能决策支持系统。" ]
  [ "沈汝超（1989- ），男，上海国际港务（集团）股份有限公司工程师，主要研究方向为港口科技管理。" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(72071145);国家重点研发计划资助项目;The National Key Research and Development Program of China(2019YFB1704402)
- DOI：10.11959/j.issn.2096-0271.2021022
  中图分类号： TP391
- 网络首发：2021-05，
  
  纸质出版：2021-05-15
- 稿件说明：
移动端阅览
郑慎鹏, 陈晓军, 向阳, 等. 基于主体掩码的实体关系抽取方法[J]. 大数据, 2021,7(3):2021022.

Shenpeng ZHENG, Xiaojun CHEN, Yang XIANG, et al. An entity relation extraction method based on subject mask[J]. Big data research, 2021, 7(3): 2021022.
郑慎鹏, 陈晓军, 向阳, 等. 基于主体掩码的实体关系抽取方法[J]. 大数据, 2021,7(3):2021022. DOI： 10.11959/j.issn.2096-0271.2021022.

Shenpeng ZHENG, Xiaojun CHEN, Yang XIANG, et al. An entity relation extraction method based on subject mask[J]. Big data research, 2021, 7(3): 2021022. DOI： 10.11959/j.issn.2096-0271.2021022.

摘要

实体关系抽取技术能够自动化地从海量无结构文本中抽取信息，构建大规模知识图谱，丰富现有知识图谱的内容，为知识图谱推理和应用提供支持。目前级联式的实体关系抽取技术已经取得了不错的成绩，但其在主体的向量表示和指针网络解码上存在不足。针对主体向量表示问题，提出利用注意力机制和掩码机制生成主体向量的方法。另外，针对指针网络中因遗漏标注而解码出过长实体的问题，提出引入实体序列标记任务，辅助指针网络解码实体。在大规模实体关系数据集DuIE2.0上进行实验验证得出，相较于先前模型，所提方法取得了0.88%的提升。

Abstract

Entity relationship extraction technology can automatically extract information from massive unstructured texts to construct large-scale knowledge graph

enrich the content of existing knowledge graph

and provide support for reasoning and application of knowledge graph.Although the cascading entity relation extraction technology has achieved good results

it has some shortcomings in the vector representation of the subject and the decoding of pointer network.In order to solve the representation problem of subject vectors

attention mechanism and mask mechanism were used to generate subject vectors.In addition

to solve the problem that long entities have been decoded in pointer network due to missing label

an entity sequence marker task was introduced to assist pointer network decoding entities.There is a 0.88% improvement over the previous model on the large-scale entity relationship dataset DuIE 2.0.

关键词

Keywords

references

邹艳珍 , 王敏 , 谢冰 , 等 . 基于大数据的软件项目知识图谱构造及问答方法 [J ] . 大数据 , 2021 , 7 ( 1 ): 22 - 36 .

ZOU Y Z , WANG M , XIE B , et al . Software knowledge graph construction and Q＆A technology based on big data [J ] . Big Data Research , 2021 , 7 ( 1 ): 22 - 36 .

陈成 , 陈跃国 , 刘宸 , 等 . 意图知识图谱的构建与应用 [J ] . 大数据 , 2020 , 6 ( 2 ): 57 - 68 .

CHEN C , CHEN Y G , LIU C , et al . Constructing and analyzing intention knowledge graphs [J ] . Big Data Research , 2020 , 6 ( 2 ): 57 - 68 .

AITKEN J S , . Learning information extraction rules:an inductive logic programming approach [C ] // Proceedings of ECAI .[S.l.:s.n ] , 2002 : 355 - 359 .

AONE C,RAMOS-SANTACRUZ M , . REES:a large-scale relation and event extraction system [C ] // Proceedings of the 6th Conference on Applied Natural Language Processing .[S.l.:s.n. ] , 2000 : 76 - 83 .

IRIA J , . T-rex:a flexible relation extraction framework [C ] // Proceedings of the 8th Annual Colloquium for the UK Special Interest Group for Computational Linguistics .[S.l.:s.n. ] , 2005 .

JIANG J , ZHAI C X . A systematic exploration of the feature space for relation extraction [C ] // Proceedings of the Main Conference on Human Language Technologies 2007:The Conference of the North American Chapter of the Association for Computational Linguistics . Stroudsburg:ACL Press , 2007 : 113 - 120 .

SUN X , DONG L H . Feature-based approach to Chinese term relation extraction [C ] // Proceedings of the 2009 International Conference on Signal Processing Systems . Piscataway:IEEE Press , 2009 : 410 - 414 .

YAN X , 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 .[S.l.:s.n. ] , 2015 : 1785 - 1794 .

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:ACL Press , 2019 : 241 - 251 .

ZHONG Z X , CHEN D Q . A frustratingly easy approach for joint entity and relation extraction [J ] . arXiv preprint , 2020 ,arXiv:2010.12812.

WANG J , SHOU L D , CHEN K , et al . Pyramid:a layered model for nested named entity recognition [C ] // Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics . Stroudsburg:ACL Press , 2020 : 5918 - 5928 .

ZHENG C M , CAI Y , XU J Y , et al . A boundary-aware neural model for nested named entity recognition [C ] // Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing . Stroudsburg:ACL Press , 2019 : 357 - 366 .

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:ACL Press , 2020 : 1476 - 1488 .

HINTON G E , SALAKHUTDINOV R R . Reducing the dimensionality of data with neural networks [J ] . Science , 2006 , 313 ( 5786 ): 504 - 507 .

VASWANI A , SHAZEER N , PARMAR N , et al . Attention is all you need [C ] // Proceedings of the 31st International Conference on Neural Information Processing Systems . New York:ACM Press , 2017 .

DEVLIN J , CHANG M W , LEE K , et al . BERT:pre-training of deep bidirectional transformers for language understanding [J ] . arXiv preprint , 2018 ,arXiv:1810.04805.

LIU Y H , OTT M , GOYAL N , et al . RoBERTa:a robustly optimized bert pretraining approach [J ] . arXiv preprint , 2019 ,arXiv:1907.11692.

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 . Stroudsburg:ACL Press , 2014 : 2335 - 2344 .

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:ACL Press , 2012 : 1201 - 1211 .

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:ACL Press , 2019 : 1409 - 1418 .

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:ACL Press , 2016 : 1105 - 1116 .

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:ACL Press , 2017 : 1227 - 1236 .

LI X Y , YIN F , SUN Z J , et al . Entityrelation extraction as multi-turn question answering [C ] // Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics . Stroudsburg:ACL Press , 2019 : 1340 - 1350 .

LI S J , HE W , SHI Y B , et al . DuIE:a largescale Chinese dataset for information extraction [C ] // Proceedings of the 8th CCF International Conference on Natural Language Processing and Chinese Computing .[S.l.:s.n ] , 2019 : 791 - 800 .

浏览量

776

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

暂无数据