Towards an Intelligent Multilingual Keyboard System

Towards an Intelligent Multilingual Keyboard System Tanapong Potipiti, Virach Sornlertlamvanich, Kanokwut Thanadkran National Electronics and Computer Technology Center, National Science and Technology Development Agency, Ministry of Science and Technology Environment, 22 nd Floor Gypsum Metropolitan Tower 539/2 Sriayudhya Rd. Rajthevi Bangkok 10400 Thailand Email: tanapong@nectec.or.th, virach@nectec.or.th, kanokwutt@notes.nectec.or.th ABSTRACT This paper proposes a practical approach employing n-gram models and error-correction rules for Thai key prediction and Thai-English language identification. The paper also proposes rule-reduction algorithm applying mutual information to reduce the error-correction rules. Our algorithm reported more than 99% accuracy in both language identification and key prediction. 1 INTRODUCTION For Thai users, there are always two annoyances while typing Thai-English bilingual documents, which are usual for Thais. The first is when the users want to switch from typing Thai to English, they have to input a special key to tell the operating system to change the language mode. Further, if the language- switching key is ignored, they have to delete the token just typed and re-type that token after language switching. The second is that Thai has more than 100 alphabets, to input about half of all Thai characters, the user has to use combinations of two keys (shift key + another key) to input them. Some of the other Asian users also have the same problem. It will be wonderful, if there is a intelligent keyboard system that is able to perform these two tasks –switching language and shifting key– automatically. This paper proposes a practical solution for these disturbances by applying trigram character probabilistic model and error-correction rules. To optimize number of the generated error-correction rules, we propose a rule reduction approach using mutual information. More than 99 percent of key prediction accuracy results are reported. 2 RELATED WORKS There is only one related work on inputting Chinese words through 0-9 numpad keys. [8] applied lexical trees and Chinese word n-grams to word prediction for inputting Chinese sentences by using digit keys. They reported 94.4% prediction accuracy. However, they did not deal with automatic language identification process. The lexical trees they employed required a large amount of space. Their algorithm is required some improvement for a practical use. 3 THE APPROACH 3.1 Overview In the traditional Thai keyboard input system, a key button with the help of language-switching key and the shift key can output 4 different characters. For example, in the Thai keyboard the ‘a’- key button can represent 4 different characters in different modes as shown in Table 1. Table 1: A key button can represent different characters in different modes. English Mode without Shift English Mode with Shift Thai Mode without Shift Thai Mode with Shift ‘a’ ‘A’ ‘�’ ‘�’ However, using NLP technique, the Thai-English keyboard system which can predict the key users intend to type without the language-selection key and the shift key, should be efficiently implemented. We propose an intelligent keyboard system to solve this problem and have implemented with successful result. To solve this problem, there are basically two steps: language identification …

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