Natural language processing (NLP) in management research: A literature review

Kang, Youngjong; Cai, Zhao; Tan, Chee‐Wee; Huang, Qian; Liu, Hefu

doi:10.1080/23270012.2020.1756939

Cited by 231 publications

(136 citation statements)

References 77 publications

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Section: Natural Language Processing (Nlp)mentioning

confidence: 99%

“…NLP is a technique for automatically understanding and analysing human language (Kang et al 2020). With the advent of artificial intelligence (AI), NLP has found applications in diverse fields (Al-Hawari and Barham 2019;Kang et al 2020). Examples of common functionalities of NLP are presented in Table 1, organised according to the sequence of appearance in typical usage.…”

Section: Natural Language Processing (Nlp)mentioning

confidence: 99%

“…Translating input text or speech into another language, e.g., English to French Kang et al (2020) Machine transformation Transforming input text or speech into another form, e.g., speech to text Alshemali and Kalita (2020) and Chowdhary (2020) Question answering Providing answers as outputs to input questions in human language (text or speech) Alshemali and Kalita (2020) Textual entailment Predicting whether a hypothesis can be inferred from an input premise Alshemali and Kalita (2020) and Feyisetan et al (2020) Tagging Identifying tokens in the input and then classifying each token according to the gram-…”

Section: Machine Translationmentioning

confidence: 99%

“…Hence, there can be several useful application scenarios of NLP in ITO. For example, as the industry is striving to automate ITO service delivery, NLP can help to improve the efficiency of collaboration among the client and service provider teams or to identify vulnerabilities in the software specification documentation produced by service provider organisations for their clients (Kang et al 2020;Zhang et al 2019). Similarly, NLP can be used to ensure privacy-preservation in multi-party ITO relationships (Sadat et al 2019).…”

Section: Introductionmentioning

confidence: 99%

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Information security implications of using NLP in IT outsourcing: a Diffusion of Innovation theory perspective

2021

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Information technology outsourcing (ITO) is a USD multi-trillion industry. There is growing competition among ITO service providers to improve their service deliveries. Natural language processing (NLP) is a technique, which can be leveraged to gain a competitive advantage in the ITO industry. This paper explores the information security implications of using NLP in ITO. First, it explores the use of NLP to enhance information security risk management (ISRM) in ITO. Then, it delves into the information security risks (ISRs) that may arise from the use of NLP in ITO. Finally, it proposes possible ISRM approaches to address those ISRs in ITO from the use of NLP. The study follows a qualitative approach using the case study method. Nine participants from three organisations (an ITO client, service provider and sub-contractor) engaged in an ITO relationship in the ICT industry were interviewed through a semi-structured questionnaire. The research findings were verified through a focus group. Case study scenarios are provided for a clear understanding of the findings. To the best of our knowledge, it is the first study to investigate the information security implications of the use of NLP in ITO.

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Section: Natural Language Processing (Nlp)mentioning

confidence: 99%

Section: Natural Language Processing (Nlp)mentioning

confidence: 99%

Section: Machine Translationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Information security implications of using NLP in IT outsourcing: a Diffusion of Innovation theory perspective

2021

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“…Recent studies on the use of deep machine learning in the field of natural language processing (NLP) and text-mining (Kang, et al, 2020;Moschitt, 2004) have shown that statistical methods can be more effective (Grekhov, 2012) when used in combination with linguistic (morphological and parsing) analysis (Khalezova, et al, 2020). This concept has given rise to a separate direction in linguistics, which studies language based on statistical regularities, including the use of linguistic and semantic analysis, expanding the statistical approach to text analysis through the use of latent semantic connections between text elements (Maheshan, et al, 2018;McCann et al, 2017;Yang, et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Automatic Identification of Authors’ Stylistics and Gender on the Basis of the Corpus of Russian Fiction Using Extended Set-theoretic Model with Collocation Extraction

Osochkin¹,

Piotrowska²,

Fomin³

2021

Glottometrics

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We present a novel quantitative approach for classification of authors' stylistics and gender differences based on extraction of word collocation. The proposed algorithm attenuates previously described issues of text processing using the vector models. We demonstrate the approach by analyzing a corpus of Russian prose. We discuss different approaches for classification and identification of the author's style implemented by currently-available software solutions and libraries of morphological analysis, methods of parameterization, indexing of texts, artificial intelligence algorithms and knowledge extraction. Our results demonstrate the efficiency and relative advantage of regression decision tree methods in identifying informative frequency indexes in a way that lends itself to their logical interpretation. We develop a toolkit for conducting comparative experiments to assess the effectiveness of classification of natural language text data, using vector, set-theoretic and the author's set-theoretic with collocation extraction models of text representation. Comparing the ability of different methods to identify the style and gender differences of authors of fiction works, we find that the proposed approach incorporating collocation information alleviates some of the previously identified deficiencies and yields overall improvements in the classification accuracy.

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Intelligent Material Data Preparation Mechanism Based on Ensemble Learning for AI‐Based Ceramic Material Analysis

Imran

Iqbal

Kim

2022

Advcd Theory and Sims

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Research interest in ceramic materials increased due to their extensive environmental, biomedical, and electronic applications. Increased demand for ceramics with specialized experimental conditions and limited resources has resulted in a higher cost for scientific practices and applications. Enormous material data is accumulated from traditional and high-tech experimentation, but the manual recording process has shown inconsistencies in the analysis results. Recently, application based on artificial intelligence (AI) and machine learning has been able to address the issues of traditional scientific experiments in material science. However, no machine learning mechanisms are proposed for sophisticated data preparation and AI-based discovery application of ceramics. This paper proposed an intelligent material data preparation mechanism based on ensemble learning for AI-assisted material screening and discovery. The current method can potentially resolve the problems of missing and inconsistent material data. As a case study, a material data preparation platform for ceramic material data pre-processing is developed. For performance evaluation of the proposed mechanism, machine learning regression models are trained before and after the imputation techniques applied to the data. Performance analysis shows that the ensemble model of deep learning network (DNN) and automated machine learning (autoML) performed better as compared to previously reported imputation approaches.

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Natural language processing (NLP) in management research: A literature review

Cited by 231 publications

References 77 publications

Information security implications of using NLP in IT outsourcing: a Diffusion of Innovation theory perspective

Information security implications of using NLP in IT outsourcing: a Diffusion of Innovation theory perspective

Automatic Identification of Authors’ Stylistics and Gender on the Basis of the Corpus of Russian Fiction Using Extended Set-theoretic Model with Collocation Extraction

Intelligent Material Data Preparation Mechanism Based on Ensemble Learning for AI‐Based Ceramic Material Analysis

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