Modeling Personal Biases in Language Use by Inducing Personalized Word Embeddings
Daisuke Oba, Naoki Yoshinaga, Shoetsu Sato, Satoshi Akasaki, Masashi Toyoda
Abstract
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. To prevent personalized word embeddings from being contaminated by other irrelevant biases, we solve a task of identifying a review-target (objective output) from a given review. To stabilize the training of this extreme multi-class classification, we perform a multi-task learning with metadata identification. Experimental results with reviews retrieved from RateBeer confirmed that the obtained personalized word embeddings improved the accuracy of sentiment analysis as well as the target task. Analysis of the obtained personalized word embeddings revealed trends in semantic variations related to frequent and adjective words.- Anthology ID:
- N19-1215
- Volume:
- Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
- Month:
- June
- Year:
- 2019
- Address:
- Minneapolis, Minnesota
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2102–2108
- Language:
- URL:
- https://aclanthology.org/N19-1215
- DOI:
- 10.18653/v1/N19-1215
- Cite (ACL):
- Daisuke Oba, Naoki Yoshinaga, Shoetsu Sato, Satoshi Akasaki, and Masashi Toyoda. 2019. Modeling Personal Biases in Language Use by Inducing Personalized Word Embeddings. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 2102–2108, Minneapolis, Minnesota. Association for Computational Linguistics.
- Cite (Informal):
- Modeling Personal Biases in Language Use by Inducing Personalized Word Embeddings (Oba et al., NAACL 2019)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/N19-1215.pdf