UPSI Digital Repository (UDRep)
|
![]() |
|
|
Abstract : Universiti Pendidikan Sultan Idris |
Corpus linguistics investigates language using extensive text databases. Tools assist researchers in analyzing, extracting, and interpreting linguistic information efficiently. Furthermore, if researchers only use traditional tools in corpus linguistic analysis, they will lack the comprehensiveness and efficiency required to effectively navigate and derive valuable insights from language data. This paper employed the preferred reporting items for systematic reviews and meta-analyses (PRISMA) approach to find the primary data based on a few keywords in corpus linguistic, corpus analysis, computational linguistic, text corpora and tool support. Based on this method, we used advanced searching techniques on Scopus and Web of Science (WoS) and discovered (N=28) data pertinent to the study. Expert scholars decide on a theme based on the problem, which is (i) types of corpus tools and their uses; (ii) their contributions and their capabilities, and (iii) limitations of corpus tools. All the tools were used in interdisciplinary studies. In summary, this systematic review uncovers pivotal key findings at the intersection of computational tools and corpus analysis, enriching linguistic knowledge. It highlights the interdisciplinary potential of corpus-based analysis in advancing linguistic tools and, their applications, as well as language analysis. © 2024 Institute of Advanced Engineering and Science. All rights reserved. |
References |
X. Lu, Computational methods for corpus annotation and analysis, vol. 9789401786454. Springer New York Heidelberg Dordrecht London, 2014. doi: 10.1007/978-94-017-8645-4. P. M. Davies, R. J. Passonneau, S. Muresan, and Y. Gao, “Analytical techniques for developing argumentative writing in STEM: a pilot study,” IEEE Transactions on Education, vol. 65, no. 3, pp. 373–383, 2022, doi: 10.1109/TE.2021.3116202. F. Goyak, M. M. Muhammad, M. F. Zaini, W. MM. A. Ibrahim, and A. Gunsuh, “Diachronic analysis of the profane words in English song lyrics: a computational linguistics perspective,” Malaysian Journal of Music, vol. 11, no. 1, pp. 1–19, 2022, doi:10.37134/mjm.vol11.1.2.2022. W. W. Lun et al., “Analysis of covid-19 related phrases using corpus-based tools: dualisms language & technology,” Journal of Positive School Psychology, vol. 6, no. 3, pp. 5034–5044, 2022. C. Minjie, W. W. Lun, Y. Guojie, C. K. S. Singh, W. Mihat, and Y. S. May, “Global intellectual trend of corpus linguistics studies among scholars in social sciences from September 2013 – September 2021,” Asian Journal of University Education, vol. 19, no. 4, pp. 613–632, 2023, doi: 10.24191/ajue.v19i4.24615. S. Goźdź-Roszkowski, “Corpus linguistics in legal discourse,” International Journal for the Semiotics of Law, vol. 34, no. 5, pp. 1515–1540, 2021, doi: 10.1007/s11196-021-09860-8. X. Cheng and X. Shi, “A corpus-based study of the discursive construction of corporate identities by Chinese and American banks,” Contrastive Pragmatics, vol. 3, no. 2, pp. 313–335, 2021, doi: 10.1163/26660393-12340008. D. Hovy, S. Melumad, and J. J. Inman, “Wordify: a tool for discovering and differentiating consumer vocabularies,” Journal of Consumer Research, vol. 48, no. 3, pp. 394–414, 2021, doi: 10.1093/jcr/ucab018. C. Baden, C. Pipal, M. Schoonvelde, and M. A. C. G. van der Velden, “Three Gaps in computational text analysis methods for social sciences: a research agenda,” Communication Methods and Measures, vol. 16, no. 1, pp. 1–18, 2022, doi:10.1080/19312458.2021.2015574. S. M. Maci and M. Sala, Corpus linguistics and translation tools for digital humanities : research methods and applications, vol. 3. 2023. doi: 10.1515/jccall-2023-0007. V. Brezina, Statistics in corpus linguistics: a practical guide. 2018. doi: 10.1017/9781316410899. B. E. Sibarani, “What do we know about balanced scorecard and its benefit? a systematic literature review,” Jurnal Dinamika Akuntansi dan Bisnis, vol. 10, no. 1, pp. 133–148, 2023, doi: 10.24815/jdab.v10i1.29351. D. Sandra, J. Segers, and R. Giacalone, “How organizations can benefit from entrainment: a systematic literature review,” Journal of Organizational Change Management, vol. 36, no. 2, pp. 233–256, 2022, doi: 10.1108/JOCM-01-2022-0023. A. Panayi, K. Ward, A. Benhadji-Schaff, A. S. Ibanez-Lopez, A. Xia, and R. Barzilay, “Evaluation of a prototype machine learning tool to semi-automate data extraction for systematic literature reviews,” Systematic Reviews, vol. 12, no. 1, pp. 1–11, 2023, doi: 10.1186/s13643-023-02351-w. P. Martin-Rodilla and M. Sánchez, “Software support for discourse-based textual information analysis: a systematic literature review and software guidelines in practice,” Information (Switzerland), vol. 11, no. 5, 2020, doi: 10.3390/INFO11050256. J. Antidze, N. Gulua, and I. Kardava, “The software for composition of some natural languages’ words,” Lecture Notes on Software Engineering, vol. 1, no. 3, pp. 295–297, 2013, doi: 10.7763/lnse.2013.v1.64. M. J. Page et al., “The PRISMA 2020 statement: an updated guideline for reporting systematic reviews,” The BMJ, vol. 372, 2021, doi: 10.1136/bmj.n71. A. Liberati et al., “The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: explanation and elaboration,” BMJ (Clinical research ed. ), vol. 339, 2009, doi: 10.1136/bmj.b2700. D. Vojnović, “Key noun+noun collocations in the language of tourism: a corpus-based study of English and Serbian,” ELOPE: English Language Overseas Perspectives and Enquiries, vol. 18, no. 2, pp. 51–68, 2021, doi: 10.4312/ELOPE.18.2.51-68. [20] J. Buts and H. Jones, “From text to data: mediality in corpus-based translation studies,” Monografias de Traduccion e Interpretacion (MonTI), no. 13, pp. 301–329, 2021, doi: 10.6035/MonTI.2021.13.10. |
This material may be protected under Copyright Act which governs the making of photocopies or reproductions of copyrighted materials. You may use the digitized material for private study, scholarship, or research. |