Smartphone is prevalent among many people. Smartphone is used not only by personal use but also by business. However, inputting Japanese text to smartphone requires longer time than PC. For this reason, predictive input, which suggesting next words, is important to type word efficiently. On the other hands, Recurrent Neural Networks (RNNs) are very powerful sequence models. Thus, we developed the input method editor (IME), which using n-gram and a recurrent neural networks language model based predictive text input. This IME is aimed at decreasing actions of inputting text. The evaluation experiments show our method outperforms conventional Japanese IME in terms of amount of time.

Flick: Japanese input method editor using N-gram and recurrent neural network language model based predictive text input / Y. Ikegami, Y. Sakurai, E. Damiani, R. Knauf, S. Tsuruta - In: 2017 13th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS) / [a cura di] Y. Ikegami. - [s.l] : IEEE, 2018. - ISBN 9781538642832. - pp. 50-55 (( Intervento presentato al 13. convegno SITIS tenutosi a Jaipur nel 2017.

Flick: Japanese input method editor using N-gram and recurrent neural network language model based predictive text input

Ernesto Damiani;
2018

Abstract

Smartphone is prevalent among many people. Smartphone is used not only by personal use but also by business. However, inputting Japanese text to smartphone requires longer time than PC. For this reason, predictive input, which suggesting next words, is important to type word efficiently. On the other hands, Recurrent Neural Networks (RNNs) are very powerful sequence models. Thus, we developed the input method editor (IME), which using n-gram and a recurrent neural networks language model based predictive text input. This IME is aimed at decreasing actions of inputting text. The evaluation experiments show our method outperforms conventional Japanese IME in terms of amount of time.
Input method editor; N-Gram; Predictive inmut; Recurrent neuronal network; Artificial Intelligence; Computer Networks and Communications; 1707; Signal Processing
Settore INF/01 - Informatica
Department of Science and Technology Government of Rajasthan
Institute of High Performance Computing and Networking (ICAR) of the National Research Center of Italy
Laboratoire Electronique, Image et Informatique (LE2I)
Malaviya National Institute of Technology Jaipur (MNIT)
University of Bourgogne
University of Milan
Book Part (author)
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2434/593816
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