An empirical study on Natural Language Processing in identifying industry demands to recommend required qualifications in the UAE

dc.Location2021 T 58.6 U56
dc.SupervisorDr Sherief Abdalla
dc.contributor.authorSuseelan Unnithan, Kannan
dc.date.accessioned2021-08-04T07:29:16Z
dc.date.available2021-08-04T07:29:16Z
dc.date.issued2021-05
dc.description.abstractA skillful and competent workforce is one of the deciding factors of any countries economic and social growth. Developed and undeveloped countries have issues related to the availability of skilled laborers. Also, the existing workforce is struggling to upgrade their skills based on the fast-changing technologies and industry environments. These factors are boosting the unemployment rate of countries. The solution for this issue is Vocational Education and Training, which will equip the workforce with the right skills. The vocational qualifications are developed based on the industry requirements. Regularly identifying and updating the industry requirements is a manual process and is expensive and time-consuming. By extracting and processing the employment advertisements published on the web, one can analyze the skills demands in each industry and the organization’s operational effectiveness. This research oversees the application of web data extraction in the education sector. This study proves that by extracting the jobs published on the web and processing the same using the NLP technique, the industry's skill requirements can be identified and propose new qualifications to be developed.en_US
dc.identifier.other20180949
dc.identifier.urihttps://bspace.buid.ac.ae/handle/1234/1881
dc.language.isoenen_US
dc.publisherThe British University in Dubai (BUiD)en_US
dc.subjectweb extractionen_US
dc.subjectqualification developmenten_US
dc.subjectNLP techniqueen_US
dc.titleAn empirical study on Natural Language Processing in identifying industry demands to recommend required qualifications in the UAEen_US
dc.typeDissertationen_US
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