Name
Abstract | ||
|---|---|---|
How to input characters without mistakes is one of very important points for the interface, when we consider usability of electronic devices such as personal computers and smartphones. Therefore, in this paper, we examine whether we can classify mistypes for Japanese input and inform the users how they make mistakes using "Attention" and "LSTM" of machine learning methods. First, we classified the category of mistypes in Japanese input into seven types (Replacement, Removal, Exchange, Insertion, Involvement, Repetition). Second, we asked subjects to input the displayed words and collected their data. Next, we classified the data and labeled it. Finally, we made the Neural Network (LSTM + Attention) to classify the mistype data and inform how they make mistakes. As a result, the correct answer rate of the Neural Network (LSTM + Attention) was about 70% and we were able to know where point the Neural Network focus on by putting in "Attention". We are planning to use "LSTM" to correct mistype directly and use "Attention" to inform how they frequently make mistakes in the future. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/ICCE46568.2020.9043138 | 2020 IEEE International Conference on Consumer Electronics (ICCE) |
Keywords | DocType | ISSN |
mistype data,correct answer rate,neural network,LSTM + Attention,Japanese input,machine learning methods,mistype correction support | Conference | 2158-3994 |
ISBN | Citations | PageRank |
978-1-7281-5187-8 | 0 | 0.34 |
References | Authors | |
1 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ryuki Komatsu | 1 | 0 | 0.34 |
| Rin Hirakawa | 2 | 0 | 1.69 |
| Hideaki Kawano | 3 | 0 | 1.69 |
| Kenichi Nakashi | 4 | 0 | 1.01 |
| Yoshihisa Nakatoh | 5 | 29 | 16.29 |