Word Familiarity Rate Estimation Using a Bayesian Linear Mixed Model

This paper presents research on word familiarity rate estimation using the {`}Word List by Semantic Principles{'}. We collected rating information on 96,557 words in the {`}Word List by Semantic Principles{'} via Yahoo! crowdsourcing. We asked 3,392 subject participants to use their introspection to rate the familiarity of words based on the five perspectives of {`}KNOW{'}, {`}WRITE{'}, {`}READ{'}, {`}SPEAK{'}, and {`}LISTEN{'}, and each word was rated by at least 16 subject participants. We used Bayesian linear mixed models to estimate the word familiarity rates. We also explored the ratings with the semantic labels used in the {`}Word List by Semantic Principles{'}.