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Article
Publication date: 26 July 2021

Pengcheng Li, Qikai Liu, Qikai Cheng and Wei Lu

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…

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.

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Article
Publication date: 6 June 2016

Wei Lu, Xinghu Yue, Qikai Cheng and Rui Meng

The purpose of this paper is to explore the use of inverse local context analysis (ILCA) to obtain data from limited accessible data sources.

Abstract

Purpose

The purpose of this paper is to explore the use of inverse local context analysis (ILCA) to obtain data from limited accessible data sources.

Design/methodology/approach

The experimental results show that the method the authors proposed can obtain all retrieved documents from the limited accessible data source using the least number of queries.

Findings

The experimental results show that the method we proposed can obtain all retrieved documents from the limited accessible data source using the least number of queries.

Originality/value

To the best of the authors’ knowledge, this paper provides the first attempt to gather all the retrieved documents from limited accessible data source, and the efficiency and ease of implementation of the proposed solution make it feasible for practical applications. The method the authors proposed can also benefit the construction of web corpus.

Details

The Electronic Library, vol. 34 no. 3
Type: Research Article
ISSN: 0264-0473

Keywords

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Article
Publication date: 24 June 2020

Yang Zhao, Lin Wang and Yaming Zhang

The paper aims to clarify the importance of the psychological processing of contextual cues in the mining of individual attention resources. In recent years, the research…

Abstract

Purpose

The paper aims to clarify the importance of the psychological processing of contextual cues in the mining of individual attention resources. In recent years, the research of more open spatial perspective, such as spatial and scene perception, has gradually turned to the recognition of contextual cues, accumulating rich literature and becoming a hotspot of interdisciplinary research. Nevertheless, besides the fields of psychology and neuroscience, researchers in other fields lack systematic knowledge of contextual cues. The purpose of this study is to expand the research field of contextual cues.

Design/methodology/approach

We retrieved 494 papers on contextual cues from SCI/SSCI core database of the Web of Science in 1992–2019. Then, we used several bibliometric and sophisticated network analysis tools, such as HistCite, CiteSpace, VOSviewe and Pajek, to identify the time-and-space knowledge map, research hotspots, evolution process, emerging trends and primary path of contextual cues.

Findings

The paper found the core scholars, major journals, research institutions, and the popularity of citation to be closely related to the research of contextual cues. In addition, we constructed a co-word network of contextual cues, confirming the concept of behavior implementation intentions and filling in the research gap in the field of behavior science. Then, the quantitative analysis of the burst literature on contextual cues revealed that the research on it that focused more on multi-objective cues. Furthermore, an analysis of the main path helped researchers clearly understand and grasp in the development trend and evolution track of contextual cues.

Originality/value

Given academic research usually lags behind management practice, our systematic review of the literature to a certain extent make a bridge between theory and practice.

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