Word Familiarity Rate Estimation Using a Bayesian Linear Mixed Model

Masayuki Asahara


Abstract
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’.
Anthology ID:
D19-5902
Volume:
Proceedings of the First Workshop on Aggregating and Analysing Crowdsourced Annotations for NLP
Month:
November
Year:
2019
Address:
Hong Kong
Editors:
Silviu Paun, Dirk Hovy
Venue:
WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
6–14
Language:
URL:
https://aclanthology.org/D19-5902
DOI:
10.18653/v1/D19-5902
Bibkey:
Cite (ACL):
Masayuki Asahara. 2019. Word Familiarity Rate Estimation Using a Bayesian Linear Mixed Model. In Proceedings of the First Workshop on Aggregating and Analysing Crowdsourced Annotations for NLP, pages 6–14, Hong Kong. Association for Computational Linguistics.
Cite (Informal):
Word Familiarity Rate Estimation Using a Bayesian Linear Mixed Model (Asahara, 2019)
Copy Citation:
PDF:
https://aclanthology.org/D19-5902.pdf