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1 – 10 of over 78000Lin Xue and Feng Zhang
With the increasing number of Web services, correct and efficient classification of Web services is crucial to improve the efficiency of service discovery. However, existing Web…
Abstract
Purpose
With the increasing number of Web services, correct and efficient classification of Web services is crucial to improve the efficiency of service discovery. However, existing Web service classification approaches ignore the class overlap in Web services, resulting in poor accuracy of classification in practice. This paper aims to provide an approach to address this issue.
Design/methodology/approach
This paper proposes a label confusion and priori correction-based Web service classification approach. First, functional semantic representations of Web services descriptions are obtained based on BERT. Then, the ability of the model is enhanced to recognize and classify overlapping instances by using label confusion learning techniques; Finally, the predictive results are corrected based on the label prior distribution to further improve service classification effectiveness.
Findings
Experiments based on the ProgrammableWeb data set show that the proposed model demonstrates 4.3%, 3.2% and 1% improvement in Macro-F1 value compared to the ServeNet-BERT, BERT-DPCNN and CARL-NET, respectively.
Originality/value
This paper proposes a Web service classification approach for the overlapping categories of Web services and improve the accuracy of Web services classification.
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Jeong‐Hyen Kim and Kyung‐Ho Lee
This paper reports on the design of a knowledge base for an automatic classification in the library science field, by using the facet classification principles of colon…
Abstract
This paper reports on the design of a knowledge base for an automatic classification in the library science field, by using the facet classification principles of colon classification (CC). To do so, by designing and constructing a knowledge base that is able to be classified automatically, and by inputting titles or key words of volumes into the computer, it aims to create class numbers automatically through automatic subject recognition and processing of key words in titles through the facet combination method of CC. Especially, the knowledge base for classification was designed along with the principle of globe and cylinder, automatic classification which can be possible.
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The purpose of this paper is to provide an answer to a critique put forward by Szostak against a paper written by the present author.
Abstract
Purpose
The purpose of this paper is to provide an answer to a critique put forward by Szostak against a paper written by the present author.
Design/methodology/approach
The paper is based on a literature‐based conceptual analysis based on Hjørland and Nissen Pedersen and Szostak. The main points in a core theory of classification are outlined and Szostak's criticism is examined and answered.
Findings
The paper demonstrates theoretical differences between the views adduced by Hjørland and Nissen Pedersen on the one side and by Szostak on the other.
Practical implications
Theoretical clarification is important for the future development of the field.
Originality/value
The paper should be seen as one among others developing an argument for a theoretical foundation of classification informed by the theory of knowledge.
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Ning Chen, Zhenyu Zhang and An Chen
Consequence prediction is an emerging topic in safety management concerning the severity outcome of accidents. In practical applications, it is usually implemented through…
Abstract
Purpose
Consequence prediction is an emerging topic in safety management concerning the severity outcome of accidents. In practical applications, it is usually implemented through supervised learning methods; however, the evaluation of classification results remains a challenge. The previous studies mostly adopted simplex evaluation based on empirical and quantitative assessment strategies. This paper aims to shed new light on the comprehensive evaluation and comparison of diverse classification methods through visualization, clustering and ranking techniques.
Design/methodology/approach
An empirical study is conducted using 9 state-of-the-art classification methods on a real-world data set of 653 construction accidents in China for predicting the consequence with respect to 39 carefully featured factors and accident type. The proposed comprehensive evaluation enriches the interpretation of classification results from different perspectives. Furthermore, the critical factors leading to severe construction accidents are identified by analyzing the coefficients of a logistic regression model.
Findings
This paper identifies the critical factors that significantly influence the consequence of construction accidents, which include accident type (particularly collapse), improper accident reporting and handling (E21), inadequate supervision engineers (O41), no special safety department (O11), delayed or low-quality drawings (T11), unqualified contractor (C21), schedule pressure (C11), multi-level subcontracting (C22), lacking safety examination (S22), improper operation of mechanical equipment (R11) and improper construction procedure arrangement (T21). The prediction models and findings of critical factors help make safety intervention measures in a targeted way and enhance the experience of safety professionals in the construction industry.
Research limitations/implications
The empirical study using some well-known classification methods for forecasting the consequences of construction accidents provides some evidence for the comprehensive evaluation of multiple classifiers. These techniques can be used jointly with other evaluation approaches for a comprehensive understanding of the classification algorithms. Despite the limitation of specific methods used in the study, the presented methodology can be configured with other classification methods and performance metrics and even applied to other decision-making problems such as clustering.
Originality/value
This study sheds new light on the comprehensive comparison and evaluation of classification results through visualization, clustering and ranking techniques using an empirical study of consequence prediction of construction accidents. The relevance of construction accident type is discussed with the severity of accidents. The critical factors influencing the accident consequence are identified for the sake of taking prevention measures for risk reduction. The proposed method can be applied to other decision-making tasks where the evaluation is involved as an important component.
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Craig R. Scott and SoeYoon Choi
The emerging area of message classification is one of growing relevance to a wide range of organizational communicators as a variety of non-state organizations and their members…
Abstract
Purpose
The emerging area of message classification is one of growing relevance to a wide range of organizational communicators as a variety of non-state organizations and their members increasingly use and misuse various terms to restrict their communication. This includes formal classifications for data security, financial/knowledge management, human resources, and other functions as well as those used informally by organizational members. Especially in a data-rich environment where our word-processing programs, e-mail tools, and other technologies afford us opportunities to engage in classification, a wide range of people at all organizational levels may serve as custodians of their own data and thus have the ability (as well as perhaps the need) to classify messages in various ways. The purpose of this paper is to describe key classification terms ranging from those found in government (e.g. top secret, confidential) to those in the private sector (e.g. business use only, trademarked) to an even wider set of terms used informally by organizational members (e.g. personal, preliminary). The growing use of message classifications will likely create various challenges and opportunities for organizations, their members, and the broader public/society. A set of future research questions is offered for corporate communication researchers and practitioners, who are well positioned to examine this emerging phenomenon.
Design/methodology/approach
This paper draws on existing literature related to the growing use of message classifications to offer a list of classification terms and an agenda for future research.
Findings
This work describes key classification terms ranging from those found in government (e.g. top secret, confidential) to those in the private sector (e.g. business use only, trademarked) to an even wider set of terms used informally by organizational members (e.g. personal, preliminary). This expanded notion of classification will likely create various challenges and opportunities for organizations, their members, and the broader public/society.
Originality/value
The emerging area of message classification is one of growing relevance to a wide range of organizational communicators as a variety of non-state organizations and their members increasingly use and misuse various terms to restrict their communication. A set of future research questions is offered for corporate communication researchers and practitioners, who are well positioned to examine this emerging phenomenon.
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Hotel classification systems are used to convey information about facilities and services. Yet, they have been prone to criticism for overemphasizing facilities at the expense of…
Abstract
Hotel classification systems are used to convey information about facilities and services. Yet, they have been prone to criticism for overemphasizing facilities at the expense of other matters of importance to service quality. In contrast, online travel agents (OTAs) use innovative methods to evaluate satisfaction with hotels. Conventional systems will lose relevance if they do not step up to consider service aspects associated with customer satisfaction. This chapter probes five hotel classification systems along with one OTA and leverages the literature to propose an improved framework classification. This is based on nine critical areas that include service quality, infrastructure, facilities and services, human resources, sustainability, safety and security, accessibility, quality systems, and online hotel ratings.
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Khouloud Ben Ltaief and Hanen Moalla
The purpose of this study is twofold. On the one hand, it studies the impact of IFRS 9 adoption on the firm value; and on the other hand, it investigates the impact of the…
Abstract
Purpose
The purpose of this study is twofold. On the one hand, it studies the impact of IFRS 9 adoption on the firm value; and on the other hand, it investigates the impact of the classification of financial assets on the firm value.
Design/methodology/approach
The study covers a sample of 55 listed banks in the Middle Eastern and North African (MENA) region. Data is collected for three years (2017–2019).
Findings
The findings show that banks’ value is not impacted by IFRS 9 adoption but by financial assets’ classification. Firm value is positively affected by fair value through other comprehensive income assets, while it is negatively affected by amortized cost and fair value through profit or loss assets. The results of the additional analysis show consistent outcomes.
Practical implications
This research reveals important managerial implications. Priority should be given to the financial assets’ classification strategy following the adoption of IFRS 9 to boost the market valuation of banks. It may be useful for investors, managers and regulators in their decision-making.
Originality/value
This study enriches previous research as IFRS 9 is a new standard, and its adoption consequences need to be investigated. A few recent studies have focused on IFRS 9 as a whole or on other parts of IFRS 9, namely, the impairment regime and hedge accounting and concern developed contexts. However, this research adds to the knowledge of capital market studies by investigating the application of IFRS 9 in terms of classification in the MENA region.
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When decision makers encounter new assurance services that can be customized for individual clients, they must include them in their pre-existing categorization of assurance, a…
Abstract
When decision makers encounter new assurance services that can be customized for individual clients, they must include them in their pre-existing categorization of assurance, a cognitive task known as postclassification. This paper draws upon three literatures (classification research in accounting, theory of assurance, and cognitive psychology) in order to suggest how this task might be modeled and studied empirically, using the example of SysTrust™. The role of a necessary condition for successful postclassification called the category use effect (Ross, 2000), in which decision makers are reminded of pre-existing categories when they learn to use new categories, is explained.
Khaled Hamed Alyoubi, Fahd Saleh Alotaibi, Akhil Kumar, Vishal Gupta and Akashdeep Sharma
The purpose of this paper is to describe a new approach to sentence representation learning leading to text classification using Bidirectional Encoder Representations from…
Abstract
Purpose
The purpose of this paper is to describe a new approach to sentence representation learning leading to text classification using Bidirectional Encoder Representations from Transformers (BERT) embeddings. This work proposes a novel BERT-convolutional neural network (CNN)-based model for sentence representation learning and text classification. The proposed model can be used by industries that work in the area of classification of similarity scores between the texts and sentiments and opinion analysis.
Design/methodology/approach
The approach developed is based on the use of the BERT model to provide distinct features from its transformer encoder layers to the CNNs to achieve multi-layer feature fusion. To achieve multi-layer feature fusion, the distinct feature vectors of the last three layers of the BERT are passed to three separate CNN layers to generate a rich feature representation that can be used for extracting the keywords in the sentences. For sentence representation learning and text classification, the proposed model is trained and tested on the Stanford Sentiment Treebank-2 (SST-2) data set for sentiment analysis and the Quora Question Pair (QQP) data set for sentence classification. To obtain benchmark results, a selective training approach has been applied with the proposed model.
Findings
On the SST-2 data set, the proposed model achieved an accuracy of 92.90%, whereas, on the QQP data set, it achieved an accuracy of 91.51%. For other evaluation metrics such as precision, recall and F1 Score, the results obtained are overwhelming. The results with the proposed model are 1.17%–1.2% better as compared to the original BERT model on the SST-2 and QQP data sets.
Originality/value
The novelty of the proposed model lies in the multi-layer feature fusion between the last three layers of the BERT model with CNN layers and the selective training approach based on gated pruning to achieve benchmark results.
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Books serve as important information resources and provide space for reflection and identity-building for many lesbian, gay, bisexual, transgender, and queer/questioning (LGBTQ+…
Abstract
Books serve as important information resources and provide space for reflection and identity-building for many lesbian, gay, bisexual, transgender, and queer/questioning (LGBTQ+) people. Many in this community have experienced reduced feelings of isolation through engagement with the writings of others. Providing a safe space for such engagement is vital. Library and information science (LIS) professionals are in an optimal position to meet such needs, particularly when efforts are made to implement changes based on explicitly expressed concerns.
This chapter provides a case study of the LGBTQ Center of Durham, North Carolina, to illustrate how the organization is integrating the local LGBTQ+ community into its library by using the community’s own vocabulary and interests to inform the center’s practices and policies. The chapter also offers a guide to the locally responsive, LGBTQ+-specific classification system created for the LGBTQ Center of Durham’s library collection. This classification system was designed to represent library materials for its Durham and surrounding-area users in a useful, accessible, and respectful manner – a feat that the library committee did not feel could be accomplished using existing classification systems.
Building on the case study for applicability, the author makes recommendations for how LIS professionals who wish to better serve LGBTQ+ users can incorporate the community into their library and/or collection. The author provides additional suggestions for action, with varying levels of commitment, for library professionals and volunteers. Through resource development, training, collection development, and classification revision, libraries can more closely align their practices with the needs of users of all gender identities and sexual orientations.
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