Full Text:   <509>

Summary:  <9>

CLC number: TP391.1

On-line Access: 2022-10-26

Received: 2021-10-18

Revision Accepted: 2022-10-26

Crosschecked: 2022-03-03

Cited: 0

Clicked: 419

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Hongbin ZHANG

https://orcid.org/0000-0001-6568-5117

Weiwen ZHANG

https://orcid.org/0000-0002-5098-6459

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Frontiers of Information Technology & Electronic Engineering  2022 Vol.23 No.11 P.1620-1630

http://doi.org/10.1631/FITEE.2100495


Improving entity linking with two adaptive features


Author(s):  Hongbin ZHANG, Quan CHEN, Weiwen ZHANG

Affiliation(s):  School of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510006, China

Corresponding email(s):   zhangww@gdut.edu.cn

Key Words:  Entity linking, Local model, Global model, Adaptive features, Entity type


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.

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author="Hongbin ZHANG, Quan CHEN, Weiwen ZHANG",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="23",
number="11",
pages="1620-1630",
year="2022",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2100495"
}

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%A Hongbin ZHANG
%A Quan CHEN
%A Weiwen ZHANG
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%DOI 10.1631/FITEE.2100495

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T1 - Improving entity linking with two adaptive features
A1 - Hongbin ZHANG
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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.

利用两个自适应特征改进实体链接

张鸿彬,陈权,张伟文
广东工业大学计算机学院,中国广州市,510006
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。

关键词:实体链接;局部模型;全局模型;自适应特征;实体类型

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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