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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 April 2010

Mohamed Amine Chatti, Anggraeni, Matthias Jarke, Marcus Specht and Katherine Maillet

The personal learning environment driven approach to learning suggests a shift in emphasis from a teacher‐driven knowledge‐push to a learner‐driven knowledge‐pull learning…

Abstract

Purpose

The personal learning environment driven approach to learning suggests a shift in emphasis from a teacher‐driven knowledge‐push to a learner‐driven knowledge‐pull learning model. One concern with knowledge‐pull approaches is knowledge overload. The concepts of collective intelligence and the Long Tail provide a potential solution to help learners cope with the problem of knowledge overload. The paper aims to address these issues.

Design/methodology/approach

Based on these concepts, the paper proposes a filtering mechanism that taps the collective intelligence to help learners find quality in the Long Tail, thus overcoming the problem of knowledge overload.

Findings

The paper presents theoretical, design, and implementation details of PLEM, a Web 2.0 driven service for personal learning management, which acts as a Long Tail aggregator and filter for learning.

Originality/value

The primary aim of PLEM is to harness the collective intelligence and leverage social filtering methods to rank and recommend learning entities.

Details

International Journal of Web Information Systems, vol. 6 no. 1
Type: Research Article
ISSN: 1744-0084

Keywords

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Article
Publication date: 14 May 2018

Daifeng Li, Andrew Madden, Chaochun Liu, Ying Ding, Liwei Qian and Enguo Zhou

Internet technology allows millions of people to find high quality medical resources online, with the result that personal healthcare and medical services have become one…

Abstract

Purpose

Internet technology allows millions of people to find high quality medical resources online, with the result that personal healthcare and medical services have become one of the fastest growing markets in China. Data relating to healthcare search behavior may provide insights that could lead to better provision of healthcare services. However, discrepancies often arise between terminologies derived from professional medical domain knowledge and the more colloquial terms that users adopt when searching for information about ailments. This can make it difficult to match healthcare queries with doctors’ keywords in online medical searches. The paper aims to discuss these issues.

Design/methodology/approach

To help address this problem, the authors propose a transfer learning using latent factor graph (TLLFG), which can learn the descriptions of ailments used in internet searches and match them to the most appropriate formal medical keywords.

Findings

Experiments show that the TLLFG outperforms competing algorithms in incorporating both medical domain knowledge and patient-doctor Q&A data from online services into a unified latent layer capable of bridging the gap between lay enquiries and professionally expressed information sources, and make more accurate analysis of online users’ symptom descriptions. The authors conclude with a brief discussion of some of the ways in which the model may support online applications and connect offline medical services.

Practical implications

The authors used an online medical searching application to verify the proposed model. The model can bridge users’ long-tailed description with doctors’ formal medical keywords. Online experiments show that TLLFG can significantly improve the searching experience of both users and medical service providers compared with traditional machine learning methods. The research provides a helpful example of the use of domain knowledge to optimize searching or recommendation experiences.

Originality/value

The authors use transfer learning to map online users’ long-tail queries onto medical domain knowledge, significantly improving the relevance of queries and keywords in a search system reliant on sponsored links.

Details

Industrial Management & Data Systems, vol. 118 no. 4
Type: Research Article
ISSN: 0263-5577

Keywords

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Book part
Publication date: 18 April 2018

Dominique Lord and Srinivas Reddy Geedipally

Purpose – This chapter provides an overview of issues related to analysing crash data characterised by excess zero responses and/or long tails and how to overcome these…

Abstract

Purpose – This chapter provides an overview of issues related to analysing crash data characterised by excess zero responses and/or long tails and how to overcome these problems. Factors affecting excess zeros and/or long tails are discussed, as well as how they can bias the results when traditional distributions or models are used. Recently introduced multi-parameter distributions and models developed specifically for such datasets are described. The chapter is intended to guide readers on how to properly analyse crash datasets with excess zeros and long or heavy tails.

Methodology – Key references from the literature are summarised and discussed, and two examples detailing how multi-parameter distributions and models compare with the negative binomial distribution and model are presented.

Findings – In the event that the characteristics of the crash dataset cannot be changed or modified, recently introduced multi-parameter distributions and models can be used efficiently to analyse datasets characterised by excess zero responses and/or long tails. They offer a simpler way to interpret the relationship between crashes and explanatory variables, while providing better statistical performance in terms of goodness-of-fit and predictive capabilities.

Research implications – Multi-parameter models are expected to become the next series of traditional distributions and models. The research on these models is still ongoing.

Practical implications – With the advancement of computing power and Bayesian simulation methods, multi-parameter models can now be easily coded and applied to analyse crash datasets characterised by excess zero responses and/or long tails.

Details

Safe Mobility: Challenges, Methodology and Solutions
Type: Book
ISBN: 978-1-78635-223-1

Keywords

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Book part
Publication date: 19 June 2019

Yayun Yan and Sampan Nettayanun

Our study explores friction costs in terms of competition and market structure, considering factors such as market share, industry leverage levels, industry hedging…

Abstract

Our study explores friction costs in terms of competition and market structure, considering factors such as market share, industry leverage levels, industry hedging levels, number of peers, and the geographic concentration that influences reinsurance purchase in the Property and Casualty insurance industry in China. Financial factors that influence the hedging level are also included. The data are hand collected from 2008 to 2015 from the Chinese Insurance Yearbook. Using panel data analysis techniques, the results are interesting. The capital structure shows a significant negative relationship with the hedging level. Group has a negative relationship with reinsurance purchases. Assets exhibit a negative relationship with hedging levels. The hedging level has a negative relation with the individual hedging level. Insurers have less incentive to hedge because it provides less resource than leverage. The study also robustly investigates the strategic risk management separately by the financial crises.

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Book part
Publication date: 10 June 2019

Anna Copeland Wheatley and Lillie M. Hibbler-Britt

The alphabet is running out of letters to tag new generations of young people who are entering the workforce. Gen Xers are now executive managers as Gen Ys settles into…

Abstract

The alphabet is running out of letters to tag new generations of young people who are entering the workforce. Gen Xers are now executive managers as Gen Ys settles into corporate careers. But what happens when Gen Z moves into the workplace? It seems oddly appropriate that these true digital natives will close out the alphabet because they are poised to reinvent the very nature of what we think of as work and business. Globalization and automation are both decisive factors in the creation of goods and services, often with less and less human oversight. At the same time, technology is creating a new, decentralized and digitized workforce that work more as free agents than company employees. This article will examine how companies can manage the transition of a workforce that is automated and can work from anywhere.

Details

Advances in the Technology of Managing People: Contemporary Issues in Business
Type: Book
ISBN: 978-1-78973-074-6

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Article
Publication date: 1 June 1992

Brennig James

Examines the apparent stability of a “real” nervous system in comparison with the instability of diagrams of circuits representing nervous entities. Suggests that this…

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Abstract

Examines the apparent stability of a “real” nervous system in comparison with the instability of diagrams of circuits representing nervous entities. Suggests that this stability is maintained by the “push‐pull” organization within a nervous system, where some parts are at work while others rest. Asks whether conservation in this form may be the result of a “need to maintain stability at a molecular level”.

Details

Kybernetes, vol. 21 no. 6
Type: Research Article
ISSN: 0368-492X

Keywords

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Book part
Publication date: 13 December 2017

Qiongwei Ye and Baojun Ma

Internet + and Electronic Business in China is a comprehensive resource that provides insight and analysis into E-commerce in China and how it has revolutionized and…

Abstract

Internet + and Electronic Business in China is a comprehensive resource that provides insight and analysis into E-commerce in China and how it has revolutionized and continues to revolutionize business and society. Split into four distinct sections, the book first lays out the theoretical foundations and fundamental concepts of E-Business before moving on to look at internet+ innovation models and their applications in different industries such as agriculture, finance and commerce. The book then provides a comprehensive analysis of E-business platforms and their applications in China before finishing with four comprehensive case studies of major E-business projects, providing readers with successful examples of implementing E-Business entrepreneurship projects.

Internet + and Electronic Business in China is a comprehensive resource that provides insights and analysis into how E-commerce has revolutionized and continues to revolutionize business and society in China.

Details

Internet+ and Electronic Business in China: Innovation and Applications
Type: Book
ISBN: 978-1-78743-115-7

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Book part
Publication date: 22 November 2012

Akie Iriyama, Jason W. Park, Franky Supriyadi and Haibin Yang

Mergers and acquisitions (M&As) typically accelerate target top management team (TMT) executive departures. Market discipline and Relative Standing are two major and…

Abstract

Mergers and acquisitions (M&As) typically accelerate target top management team (TMT) executive departures. Market discipline and Relative Standing are two major and competing economic and sociological explanations for this phenomenon which lack a satisfactory theoretical integration. To fill this gap in the literature, we model the M&A market as a complex adaptive system composed of TMTs which rid themselves of executives via self-organized critical processes, generating M&A market-level properties that are emergent, or not easily explained with reference to the individual TMTs. The observation of an emergent power law distribution in target TMT executive retention rates for M&A activities in the United States supports our interpretation.

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Article
Publication date: 1 February 1984

B.C. BROOKES

Haitun has recently shown that empirical distributions are of two types—‘Gaussian’ and ‘Zipfian’—characterized by the presence or absence of moments. Gaussian‐type…

Abstract

Haitun has recently shown that empirical distributions are of two types—‘Gaussian’ and ‘Zipfian’—characterized by the presence or absence of moments. Gaussian‐type distributions arise only in physical contexts: Zipfian only in social contexts. As the whole of modern statistical theory is based on Gaussian distributions, Haitun thus shows that its application to social statistics, including cognitive statistics, is ‘inadmissible’. A new statistical theory based on ‘Zipfian’ distributions is therefore needed for the social sciences. Laplace's notorious ‘law of succession’, which has evaded derivation by classical probability theory, is shown to be the ‘Zipfian’ frequency analogue of the Bradford law. It is argued that these two laws together provide the most convenient analytical instruments for the exploration of social science data. Some implications of these findings for the quantitative analysis of information systems are briefly discussed.

Details

Journal of Documentation, vol. 40 no. 2
Type: Research Article
ISSN: 0022-0418

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