Personalized reading support for second-language web documents by collective intelligence

Authors:
Yo Ehara;Nobuyuki Shimizu;Takashi Ninomiya;Hiroshi Nakagawa
Affiliations:
The University of Tokyo, Tokyo, Japan;The University of Tokyo, Tokyo, Japan;The University of Tokyo, Tokyo, Japan;The University of Tokyo, Tokyo, Japan
Venue:
Proceedings of the 15th international conference on Intelligent user interfaces
Year:
2010

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Abstract

Novel intelligent interface eases the browsing of Web documents written in the second languages of users. It automatically predicts words unfamiliar to the user by collective intelligence and glosses them with their meaning in advance. If the prediction succeeds, the user does not need to consult a dictionary; even if it fails, the user can correct the prediction. The correction data are collected and used to improve the accuracy of further predictions. The prediction is personalized in that every user's language ability is estimated by a state-of-the-art language testing model, which is trained in a practical response time with only a small sacrifice of prediction accuracy. Evaluation results for the system in terms of prediction accuracy are encouraging.