Date of Award
2017
Document Type
Thesis
Degree Name
Bachelors
Department
Natural Sciences
First Advisor
Gillman, David
Area of Concentration
Computer Science
Abstract
An advanced learner of the language or native speaker can extrapolate the pronunciations for a given kanji to estimate the reading of a never before seen word. Most textbooks discourage learners of Japanese from finding patterns and instead advise memorization of a kanji in each context to gain proficiency in learning. I disagree with this approach and designed a Naive Bayes Classifier that is trained on a simple Japanese dictionary. The model is able to impute the pronunciation of Japanese kanji with 90% accuracy in limited testing. The probabilities from the Naive Bayes Classifier can be easily read and understood by a Japanese learner to quickly gain proficiency in the language. As the end product, my model will help learners gain the intuition needed to estimate the pronunciation of a given kanji in an unfamiliar context.
Recommended Citation
Duffrin, David, "INTUITION BEHIND KANJI" (2017). Theses & ETDs. 5341.
https://digitalcommons.ncf.edu/theses_etds/5341