A Bibliometric Perspective on Technology-Driven Innovation in the Gulf Cooperation Council (GCC) Countries in Relation to Its Transformative Impact on International Business

Sarwar, Raheem; Soroya, Saira Hanif; Muazzam, Amina; Sabah, Fahad; Iqbal, Sehrish; Hassan, Saeed-Ul

doi:10.4018/978-1-5225-9012-5.ch003

Cited by 8 publications

(3 citation statements)

References 13 publications

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“…Natural language processing (NLP) is a subfeld of computer science, linguistics, and information engineering, concerned with the interactions between computers and human languages [3,4,12,13,27,28,31,32,35,36,41,44,47]. In context of the objectives of this investigation given in Section Introduction, we organize the literature review in following two sections.…”

Section: Literature Reviewmentioning

confidence: 99%

Native Language Identification of Fluent and Advanced Non-Native Writers

Sarwar

Rutherford

Hassan

et al. 2020

ACM Trans. Asian Low-Resour. Lang. Inf. Process.

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Native Language Identification (NLI) aims at identifying the native languages of authors by analyzing their text samples written in a non-native language. Most existing studies investigate this task for educational applications such as second language acquisition and require the learner corpora. This article performs NLI in a challenging context of the user-generated-content (UGC) where authors are fluent and advanced non-native speakers of a second language. Existing NLI studies with UGC (i) rely on the content-specific/social-network features and may not be generalizable to other domains and datasets, (ii) are unable to capture the variations of the language-usage-patterns within a text sample, and (iii) are not associated with any outlier handling mechanism. Moreover, since there is a sizable number of people who have acquired non-English second languages due to the economic and immigration policies, there is a need to gauge the applicability of NLI with UGC to other languages. Unlike existing solutions, we define a topic-independent feature space, which makes our solution generalizable to other domains and datasets. Based on our feature space, we present a solution that mitigates the effect of outliers in the data and helps capture the variations of the language-usage-patterns within a text sample. Specifically, we represent each text sample as a point set and identify the top- k stylistically similar text samples (SSTs) from the corpus. We then apply the probabilistic k nearest neighbors’ classifier on the identified top- k SSTs to predict the native languages of the authors. To conduct experiments, we create three new corpora where each corpus is written in a different language, namely, English, French , and German . Our experimental studies show that our solution outperforms competitive methods and reports more than 80% accuracy across languages.

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Section: Literature Reviewmentioning

confidence: 99%

Native Language Identification of Fluent and Advanced Non-Native Writers

Sarwar

Rutherford

Hassan

et al. 2020

ACM Trans. Asian Low-Resour. Lang. Inf. Process.

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“…The applications of long-text authorship identifcation can be found in several areas associated with managing large text repositories and plagiarism detection in student theses. Specifcally, retrieving and categorizing documents with respect to their authors and plagiarism detection have been receiving signifcant attention by researchers in several areas such as natural language processing [2, 3, 34], web information management [25] and information retrieval [10,26,28,30,31,38].…”

Section: Thai Authorship Identification (Svm-cwpest)mentioning

confidence: 99%

StyloThai:

Sarwar

Porthaveepong

Rutherford

et al. 2020

ACM Trans. Asian Low-Resour. Lang. Inf. Process.

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Authorship identification helps to identify the true author of a given anonymous document from a set of candidate authors. The applications of this task can be found in several domains, such as law enforcement agencies and information retrieval. These application domains are not limited to a specific language, community, or ethnicity. However, most of the existing solutions are designed for English, and a little attention has been paid to Thai. These existing solutions are not directly applicable to Thai due to the linguistic differences between these two languages. Moreover, the existing solution designed for Thai is unable to (i) handle outliers in the dataset, (ii) scale when the size of the candidate authors set increases, and (iii) perform well when the number of writing samples for each candidate author is low. We identify a stylometric feature space for the Thai authorship identification task. Based on our feature space, we present an authorship identification solution that uses the probabilistic k nearest neighbors classifier by transforming each document into a collection of point sets. Specifically, this document transformation allows us to (i) use set distance measures associated with an outlier handling mechanism, (ii) capture stylistic variations within a document, and (iii) produce multiple predictions for a query document. We create a new Thai authorship identification corpus containing 547 documents from 200 authors, which is significantly larger than the corpus used by the existing study (an increase of 32 folds in terms of the number of candidate authors). The experimental results show that our solution can overcome the limitations of the existing solution and outperforms all competitors with an accuracy level of 91.02%. Moreover, we investigate the effectiveness of each stylometric features category with the help of an ablation study. We found that combining all categories of the stylometric features outperforms the other combinations. Finally, we cross compare the feature spaces and classification methods of all solutions. We found that (i) our solution can scale as the number of candidate authors increases, (ii) our method outperforms all the competitors, and (iii) our feature space provides better performance than the feature space used by the existing study.

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“…As well as their innovation and research potential, Saudi institutions' development of students' skills and capacities [3] has improved dramatically [4], suggesting a shift to digital transformation and sustainable innovation in the Kingdom of Saudi Arabia (KSA). Another shift observed in Saudi institutions has been the high-quality work published through stronger collaboration with the international and regional scientific community [5,6].…”

Section: Introductionmentioning

confidence: 99%

A Methodological Framework to Predict Future Market Needs for Sustainable Skills Management Using AI and Big Data Technologies

et al. 2022

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Analysing big data job posts in Saudi cyberspace to describe the future market need for sustainable skills, this study used the power of artificial intelligence, deep learning, and big data technologies. The study targeted three main stakeholders: students, universities, and job providers. It provides analytical insights to improve student satisfaction, retention, and employability, investigating recent trends in the essential skills pinpointed as enhancing the social effect of learning, and identifying and developing the competencies and talents required for the Kingdom of Saudi Arabia’s (KSA’s) digital transformation into a regional and global leader in technology-driven innovation. The methodological framework comprises smart data processing, word embedding, and case-based reasoning to identify the skills required for job positions. The study’s outcomes may promote the alignment of KSA’s business and industry to academia, highlighting where to build competencies and skills. They may facilitate the parameterisation of the learning process, boost universities’ ability to promote learning efficiency, and foster the labour market’s sustainable evolution towards technology-driven innovation. We believe that this study is crucial to Vision 2030’s realisation through a long-term, inclusive approach to KSA’s transformation of knowledge and research into new employment, innovation, and capacity.

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A Bibliometric Perspective on Technology-Driven Innovation in the Gulf Cooperation Council (GCC) Countries in Relation to Its Transformative Impact on International Business

Cited by 8 publications

References 13 publications

Native Language Identification of Fluent and Advanced Non-Native Writers

Native Language Identification of Fluent and Advanced Non-Native Writers

StyloThai:

A Methodological Framework to Predict Future Market Needs for Sustainable Skills Management Using AI and Big Data Technologies

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