Modeling Personal Biases in Language Use by Inducing Personalized Word Embeddings

NAACL 2019 Daisuke ObaNaoki YoshinagaShoetsu SatoSatoshi AkasakiMasashi Toyoda

There exist biases in individual{'}s language use; the same word (e.g., cool) is used for expressing different meanings (e.g., temperature range) or different words (e.g., cloudy, hazy) are used for describing the same meaning. In this study, we propose a method of modeling such personal biases in word meanings (hereafter, semantic variations) with personalized word embeddings obtained by solving a task on subjective text while regarding words used by different individuals as different words... (read more)

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