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Data set entity recognition based on distant supervision

Pengcheng Li (School of Information Management, Wuhan University, Wuhan, Hubei, China)
Qikai Liu (School of Information Management, Wuhan University, Wuhan, Hubei, China)
Qikai Cheng (School of Information Management, Wuhan University, Wuhan, Hubei, China)
Wei Lu (School of Information Management, Wuhan University, Wuhan, Hubei, China)

The Electronic Library

ISSN: 0264-0473

Article publication date: 26 July 2021

Issue publication date: 4 November 2021

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Abstract

Purpose

This paper aims to identify data set entities in scientific literature. To address poor recognition caused by a lack of training corpora in existing studies, a distant supervised learning-based approach is proposed to identify data set entities automatically from large-scale scientific literature in an open domain.

Design/methodology/approach

Firstly, the authors use a dictionary combined with a bootstrapping strategy to create a labelled corpus to apply supervised learning. Secondly, a bidirectional encoder representation from transformers (BERT)-based neural model was applied to identify data set entities in the scientific literature automatically. Finally, two data augmentation techniques, entity replacement and entity masking, were introduced to enhance the model generalisability and improve the recognition of data set entities.

Findings

In the absence of training data, the proposed method can effectively identify data set entities in large-scale scientific papers. The BERT-based vectorised representation and data augmentation techniques enable significant improvements in the generality and robustness of named entity recognition models, especially in long-tailed data set entity recognition.

Originality/value

This paper provides a practical research method for automatically recognising data set entities in scientific literature. To the best of the authors’ knowledge, this is the first attempt to apply distant learning to the study of data set entity recognition. The authors introduce a robust vectorised representation and two data augmentation strategies (entity replacement and entity masking) to address the problem inherent in distant supervised learning methods, which the existing research has mostly ignored. The experimental results demonstrate that our approach effectively improves the recognition of data set entities, especially long-tailed data set entities.

Keywords

Citation

Li, P., Liu, Q., Cheng, Q. and Lu, W. (2021), "Data set entity recognition based on distant supervision", The Electronic Library, Vol. 39 No. 3, pp. 435-449. https://doi.org/10.1108/EL-10-2020-0301

Publisher

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Emerald Publishing Limited

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