利用两个自适应特征改进实体链接

张鸿彬; 陈权; 张伟文

doi:10.1631/FITEE.2100495

Your Location：

Home >

Browse articles >

利用两个自适应特征改进实体链接

常规文章 | Updated：2023-01-11

- 利用两个自适应特征改进实体链接
- Improving entity linking with two adaptive features
- 信息与电子工程前沿（英文） 2022年23卷第11期页码：1620-1630
- Affiliations：
  
  School of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510006, China
- Author bio：
  
  ‡Corresponding author
- Funds：
  
  Key-Area Research and Development Program of Guangdong Province, China(2019B010153002);Program of Marine Economy Development (Six Marine Industries) Special Foundation of Department of Natural Resources of Guangdong Province, China(GDNRC [2020]056);National Natural Science Foundation of China(62002071);Top Youth Talent Project of Zhujiang Talent Program, China(2019QN01X516);Guangdong Provincial Key Laboratory of Cyber-Physical System, China(2020B1212060069)
- DOI：10.1631/FITEE.2100495
  中图分类号： TP391.1
- 收稿：2021-10-18，
  
  录用：2022-03-03，
  
  网络出版：2022-09-24，
  
  纸质出版：2022-11-0
- Accepted：
Scan QR Code
张鸿彬, 陈权, 张伟文. 利用两个自适应特征改进实体链接[J]. 信息与电子工程前沿（英文）, 2022,23(11):1620-1630.

Hongbin ZHANG, Quan CHEN, Weiwen ZHANG. Improving entity linking with two adaptive features[J]. Frontiers of Information Technology & Electronic Engineering, 2022, 23(11): 1620-1630.
张鸿彬, 陈权, 张伟文. 利用两个自适应特征改进实体链接[J]. 信息与电子工程前沿（英文）, 2022,23(11):1620-1630. DOI： 10.1631/FITEE.2100495.

Hongbin ZHANG, Quan CHEN, Weiwen ZHANG. Improving entity linking with two adaptive features[J]. Frontiers of Information Technology & Electronic Engineering, 2022, 23(11): 1620-1630. DOI： 10.1631/FITEE.2100495.

摘要

实体链接是自然语言处理中的一项基本任务。现有的基于神经网络的系统更多地关注全局模型的构建，而忽略了局部模型中潜在的语义信息和有效实体类型信息的获取。本文提出两个自适应特征，其中第一个自适应特征使得局部和全局模型能够捕获潜在信息，第二个自适应特征能够描述实体类型嵌入的有效信息。这些自适应特征可以很自然地协同工作来处理一些不确定的实体类型信息。实验结果表明，我们的实体链接系统在AIDA-B和MSNBC数据集上取得了最佳的性能，并在域外数据集上达到了最佳的平均性能。这些结果表明，所提出的自适应特征能够基于其自身不同的上下文来捕获有利于实体链接的信息。

Abstract

Entity linking (EL) is a fundamental task in natural language processing. Based on neural networks

existing systems pay more attention to the construction of the global model

but ignore latent semantic information in the local model and the acquisition of effective entity type information. In this paper

we propose two adaptive features

in which the first adaptive feature enables the local and global models to capture latent information

and the second adaptive feature describes effective information for entity type embeddings. These adaptive features can work together naturally to handle some uncertain entity type information for EL. Experimental results demonstrate that our EL system achieves the best performance on the AIDA-B and MSNBC datasets

and the best average performance on out-domain datasets. These results indicate that the proposed adaptive features

which are based on their own diverse contexts

can capture information that is conducive for EL.

关键词

Keywords

references

Bunescu R , Paşca M , 2006 . Using encyclopedic knowledge for named entity disambiguation . Proc 11 th Conf of the European Chapter of the Association for Computational Linguistics , p. 9 - 16 .

Cao YX , Hou L , Li JZ , et al. , 2018 . Neural collective entity linking . Proc 27 th Int Conf on Computational Linguistics , p. 675 - 686 .

Chen S , Wang JP , Jiang F , et al. , 2020 . Improving entity linking by modeling latent entity type information . Proc 34 th AAAI Conf on Artificial Intelligence , p. 7529 - 7537 . doi: 10.1609/aaai.v34i05.6251 http://doi.org/10.1609/aaai.v34i05.6251

Deng ZH , Li ZX , Yang Q , et al. , 2020 . Improving entity linking with graph networks . Proc 21 st Int Conf on Web Information Systems Engineering , p. 343 - 354 . doi: 10.1007/978-3-030-62005-9_25 http://doi.org/10.1007/978-3-030-62005-9_25

Devlin J , Chang MW , Lee K , et al. , 2019 . BERT: pre-training of deep bidirectional transformers for language understanding . Proc Conf of the North American Chapter of the Association for Computational Linguistics , p. 4171 - 4186 . doi: 10.18653/v1/N19-1423 http://doi.org/10.18653/v1/N19-1423

Durrett G , Klein D , 2014 . A joint model for entity analysis: coreference, typing, and linking . Trans Assoc Comput Linguist , 2 : 477 - 490 . doi: 10.1162/tacl_a_00197 http://doi.org/10.1162/tacl_a_00197

Fang W , Zhang JW , Wang DL , et al. , 2016 . Entity disambiguation by knowledge and text jointly embedding . Proc 20 th SIGNLL Conf on Computational Natural Language Learning , p. 260 - 269 . doi: 10.18653/v1/K16-1026 http://doi.org/10.18653/v1/K16-1026

Fang Z , Cao YN , Li Q , et al. , 2019 . Joint entity linking with deep reinforcement learning . Proc World Wide Web Conf , p. 438 - 447 . doi: 10.1145/3308558.3313517 http://doi.org/10.1145/3308558.3313517

Francis-Landau M , Durrett G , Klein D , 2016 . Capturing semantic similarity for entity linking with convolutional neural networks . Proc Conf of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies , p. 1256 - 1261 . doi: 10.18653/v1/N16-1150 http://doi.org/10.18653/v1/N16-1150

Ganea OE , Hofmann T , 2017 . Deep joint entity disambiguation with local neural attention . Proc Conf on Empirical Methods in Natural Language Processing , p. 2619 - 2629 . doi: 10.18653/v1/D17-1277 http://doi.org/10.18653/v1/D17-1277

Globerson A , Lazic N , Chakrabarti S , et al. , 2016 . Collective entity resolution with multi-focal attention . Proc 54 th Annual Meeting of the Association for Computational Linguistics , p. 621 - 631 . doi: 10.18653/v1/P16-1059 http://doi.org/10.18653/v1/P16-1059

Guo ZC , Barbosa D , 2018 . Robust named entity disambiguation with random walks . Semant Web , 9 ( 4 ): 459 - 479 . doi: 10.3233/SW-170273 http://doi.org/10.3233/SW-170273

Han XP , Sun L , 2011 . A generative entity-mention model for linking entities with knowledge base . Proc 49 th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies , p. 945 - 954 .

Hoffart J , Yosef MA , Bordino I , et al. , 2011 . Robust disambiguation of named entities in text . Proc Conf on Empirical Methods in Natural Language Processing , p. 782 - 792 .

Hoffmann R , Zhang CL , Ling X , et al. , 2011 . Knowledge-based weak supervision for information extraction of overlapping relations . Proc 49 th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies , p. 541 - 550 .

Honnibal M , Dale R , 2009 . DAMSEL: the DSTO/Macquarie system for entity-linking . Proc 2 nd Text Analysis Conf .

Hou F , Wang RL , He J , et al. , 2020 . Improving entity linking through semantic reinforced entity embeddings . Proc 58 th Annual Meeting of the Association for Computational Linguistics , p. 6843 - 6848 . doi: 10.18653/v1/2020.acl-main.612 http://doi.org/10.18653/v1/2020.acl-main.612

Le P , Titov I , 2018 . Improving entity linking by modeling latent relations between mentions . Proc 56 th Annual Meeting of the Association for Computational Linguistics , p. 1595 - 1604 . doi: 10.18653/v1/P18-1148 http://doi.org/10.18653/v1/P18-1148

Li ZZ , Feng DW , Li DS , et al. , 2020 . Learning to select pseudo labels: a semi-supervised method for named entity recognition . Front Inform Technol Electron Eng , 21 ( 6 ): 903 - 916 . doi: 10.1631/FITEE.1800743 http://doi.org/10.1631/FITEE.1800743

Luo G , Huang XJ , Lin CY , et al. , 2015 . Joint entity recognition and disambiguation . Proc Conf on Empirical Methods in Natural Language Processing , p. 879 - 888 . doi: 10.18653/v1/D15-1104 http://doi.org/10.18653/v1/D15-1104

Martins PH , Marinho Z , Martins AFT , 2019 . Joint learning of named entity recognition and entity linking . Proc 57 th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop , p. 190 - 196 . doi: 10.18653/v1/P19-2026 http://doi.org/10.18653/v1/P19-2026

Medelyan O , Milne D , Legg C , et al. , 2009 . Mining meaning from Wikipedia . Int J Hum-Comput Stud , 67 ( 9 ): 716 - 754 . doi: 10.1016/j.ijhcs.2009.05.004 http://doi.org/10.1016/j.ijhcs.2009.05.004

Milne D , Witten IH , 2008 . Learning to link with Wikipedia . Proc 17 th ACM Conf on Information and Knowledge Management , p. 509 - 518 . doi: 10.1145/1458082.1458150 http://doi.org/10.1145/1458082.1458150

Mulang IO , Singh K , Prabhu C , et al. , 2020 . Evaluating the impact of knowledge graph context on entity disambiguation models . Proc 29 th ACM Int Conf on Information & Knowledge Management , p. 2157 - 2160 . doi: 10.1145/3340531.3412159 http://doi.org/10.1145/3340531.3412159

Nguyen DB , Theobald M , Weikum G , 2016 . J-NERD: joint named entity recognition and disambiguation with rich linguistic features . Trans Assoc Comput Linguist , 4 : 215 - 229 . doi: 10.1162/tacl_a_00094 http://doi.org/10.1162/tacl_a_00094

Pennington J , Socher R , Manning C , 2014 . GloVe: global vectors for word representation . Proc Conf on Empirical Methods in Natural Language Processing , p. 1532 - 1543 . doi: 10.3115/v1/D14-1162 http://doi.org/10.3115/v1/D14-1162

Pershina M , He YF , Grishman R , 2015 . Personalized page rank for named entity disambiguation . Proc Conf of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies , p. 238 - 243 . doi: 10.3115/v1/N15-1026 http://doi.org/10.3115/v1/N15-1026

Runge A , Hovy E , 2020 . Exploring neural entity representations for semantic information . Proc 3 rd BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP , p. 204 - 216 . doi: 10.18653/v1/2020.blackboxnlp-1.20 http://doi.org/10.18653/v1/2020.blackboxnlp-1.20

Sevgili O , Shelmanov A , Arkhipov M , et al. , 2020 . Neural entity linking: a survey of models based on deep learning . https://arxiv.org/abs/2006.00575 https://arxiv.org/abs/2006.00575

Shen W , Wang JY , Han JW , 2015 . Entity linking with a knowledge base: issues, techniques, and solutions . IEEE Trans Knowl Data Eng , 27 ( 2 ): 443 - 460 . doi: 10.1109/TKDE.2014.2327028 http://doi.org/10.1109/TKDE.2014.2327028

Vaswani A , Shazeer N , Parmar N , et al. , 2017 . Attention is all you need . Proc 31 st Int Conf on Neural Information Processing Systems , p. 6000 - 6010 .

Wang HF , Liu ZQ , 2019 . An error recognition method for power equipment defect records based on knowledge graph technology . Front Inform Technol Electron Eng , 20 ( 11 ): 1564 - 1577 . doi: 10.1631/FITEE.1800260 http://doi.org/10.1631/FITEE.1800260

Wang Z , Zhang JW , Feng JL , et al. , 2014 . Knowledge graph and text jointly embedding . Proc Conf on Empirical Methods in Natural Language Processing , p. 1591 - 1601 . doi: 10.3115/v1/D14-1167 http://doi.org/10.3115/v1/D14-1167

Wu JS , Zhang RC , Mao YY , et al. , 2020 . Dynamic graph convolutional networks for entity linking . Proc Web Conf , p. 1149 - 1159 . doi: 10.1145/3366423.3380192 http://doi.org/10.1145/3366423.3380192

Xin KX , Hua W , Liu Y , et al. , 2019 . Entity disambiguation based on parse tree neighbours on graph attention network . Proc 20 th Int Conf on Web Information Systems Engineering , p. 523 - 537 . doi: 10.1007/978-3-030-34223-4_33 http://doi.org/10.1007/978-3-030-34223-4_33

Xu P , Barbosa D , 2018 . Neural fine-grained entity type classification with hierarchy-aware loss . Proc Conf of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies , p. 16 - 25 . doi: 10.18653/v1/N18-1002 http://doi.org/10.18653/v1/N18-1002

Xue MG , Cai WM , Su JS , et al. , 2019 . Neural collective entity linking based on recurrent random walk network learning . Proc 28 th Int Joint Conf on Artificial Intelligence , p. 5327 - 5333 . doi: 10.24963/ijcai.2019/740 http://doi.org/10.24963/ijcai.2019/740

Yamada I , Shindo H , Takeda H , et al. , 2016 . Joint learning of the embedding of words and entities for named entity disambiguation . Proc 20 th SIGNLL Conf on Computational Natural Language Learning , p. 250 - 259 . doi: 10.18653/v1/K16-1025 http://doi.org/10.18653/v1/K16-1025

Yang XY , Gu XT , Lin S , et al. , 2019 . Learning dynamic context augmentation for global entity linking . Proc Conf on Empirical Methods in Natural Language Processing and the 9 th Int Joint Conf on Natural Language Processing , p. 271 - 281 . doi: 10.18653/v1/D19-1026 http://doi.org/10.18653/v1/D19-1026

Zhang HB , Chen Q , Zhang WW , et al. , 2022 . HSIE: improving named entity disambiguation with hidden semantic information extractor . Proc 14 th Int Conf on Machine Learning and Computing , p. 251 - 257 . doi: 10.1145/3529836.3529920 http://doi.org/10.1145/3529836.3529920

Zhong HP , Zhang JW , Wang Z , et al. , 2015 . Aligning knowledge and text embeddings by entity descriptions . Proc Conf on Empirical Methods in Natural Language Processing , p. 267 - 272 . doi: 10.18653/v1/D15-1031 http://doi.org/10.18653/v1/D15-1031

浏览量

143

Downloads

CSCD

文章被引用时，请邮件提醒。

Submit

工具集

关联资源

暂无数据