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Development of Word-Level Classification and Vocabulary Meaning System

Kamal Baha and Makoto Shishido

Abstract—There are multiple ways to improve reading comprehension for English learners as a foreign language. Learning vocabulary is one of the ways to improve it. It is believed that the more readers are familiar with the English vocabulary, the better they will understand what they are reading. It is suggested that the learners improve comprehension if they learn and understand unknown words before they read an essay. The morphological analysis was used to extract the words from each sentence in an English text. The extracted vocabulary was also classified by level of difficulty into 12 levels according to the ALC12000 database. The system showed only the words whose levels were higher than the student's estimated vocabulary level. It will be expected that a learner can improve their English reading comprehension by studying higher-level vocabulary in advance of reading an essay. In this study, a new method was investigated in order to analyze vocabulary and develop a system that can analyze vocabulary from English texts and add Japanese and Thai meanings. To employ it for any device, the researchers developed the system by using a JavaScript library called NLP-compromise that uses any browser on any system.

Index Terms—Educational technology, computer aided instruction, system development, language processing, morphology, word-level classification.

Kamal Baha and Makoto Shishido are with Tokyo Denki University, Japan (e-mail: 20udc02@ms.dendai.ac.jp).

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Copyright © 2022 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

General Information

  • ISSN: 2010-3689 (Online)
  • Abbreviated Title: Int. J. Inf. Educ. Technol.
  • Frequency: Monthly
  • DOI: 10.18178/IJIET
  • Editor-in-Chief: Prof. Dr. Steve Thatcher
  • Executive Editor: Ms. Nancy Y. Liu
  • Abstracting/ Indexing: Scopus (CiteScore 2021: 1.3), INSPEC (IET), UGC-CARE List (India), CNKI, EBSCO, Electronic Journals Library, Google Scholar, Crossref, etc.
  • E-mail: ijiet@ejournal.net

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