Evaluation of Domain-Specific Vocabulary with Machine Learning-Based Techniques: Japanese and Russian Case Studies

Abstract

Foreign language education is one of the prominent requirements. Undergraduate students at the Faculty of Tourism are offered the opportunity to learn a second foreign language, which will contribute to their professional lives. However, this second foreign language, which is taught from the beginner level, cannot contribute to the students’ professional lives at a desired level unless it includes professional technical terms related to their profession. For this reason, foreign language education books should include field words related to the professional field to a certain extent. This study examines the suitability of foreign language education books used at the basic level in Russian and Japanese courses from the scope of their field speciality. First, the frequently used words in the fields of “Tourism and Hotel Management” and “Tourism Guidance” were determined and set as the keywords. Then, depending on these keywords, other frequently used words were obtained using machine learning and natural language processing techniques. For this purpose, we used Python’s Gensim library, and we established corpuses of word vectors consisting of both the keywords and the near-distanced words to these keywords in each field with the help of pre-trained word vector models. This study revealed statistically to what extent the textbooks currently used contain the domain-specific vocabulary in the field.

Keywords

• NLP
• Japanese
• Russian
• Foreign Language
• Tourism

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