---
title: Modeless Japanese Input Method
tags:  #学会  
author: [Yukino Ikegami](https://convert.docswell.com/user/yukinoi)
site: [Docswell](https://www.docswell.com/)
thumbnail: https://bcdn.docswell.com/page/Y76YYVNDEV.jpg?width=480
description: Presentation about Modeless Japanese Input Method
published: June 16, 15
canonical: https://convert.docswell.com/s/yukinoi/ZW1DYX-modeless-japanese-input
---
# Page. 1

![Page Image](https://bcdn.docswell.com/page/Y76YYVNDEV.jpg)

Hybrid method
for modeless Japanese input
using N-gram based binary classification
and dictionary
Yukino Ikegami
Setsuo Tsuruta
2014/01/20


# Page. 2

![Page Image](https://bcdn.docswell.com/page/G75GGD5874.jpg)

Necessity of Japanese Input Method
• Japanese has many characters
– Kana
• Hiragana
– 81 characters e.g.) いろはにほへと
• Katakana
– 81 characters e.g.) イロハニホヘト
– Kanji (Chinese-characters)
• More than 6,000 characters e.g.) 以呂波仁保反止
• We can’t input directly by a keyboard
➢ Japanese input method (Converting alphabet to
Japanese character) is necessary
2


# Page. 3

![Page Image](https://bcdn.docswell.com/page/9J2YY8NVJR.jpg)

If all Japanese characters are assigned
to each key…
• Toooo many keys!
• Japanese input method is necessary


# Page. 4

![Page Image](https://bcdn.docswell.com/page/DEYGG6PQEM.jpg)

Japanese Input Method
-Roman to Kana-Kanji Converter• Flow
1. Receive the Romanized alphabets
①n
ekodesu
2. Convert the Romanized alphabets
②ねこです
into Kana using Roman-to-Kana table
3. Convert Kana into Kanji (if necessary) ③猫です
4


# Page. 5

![Page Image](https://bcdn.docswell.com/page/VJNGGZ5278.jpg)

Problems
on Japanese Input Method
• Need to switch input modes between
Japanese and ASCII
e.g. To input ‘あれは8Byteです’ (That is 8Byte)
areha [Return][ASCII Mode] 8byte [Japanese Mode] desu
Switching
Switching
• Switching is cumbersome!
5


# Page. 6

![Page Image](https://bcdn.docswell.com/page/YE9WWZYDE3.jpg)

Adding Term to Dictionary
for Switching Mode Problem
• Adding term of other languages to dictionary
of conventional input method editor
• Shortcoming
– New term is created continuously
– Homograph problem


# Page. 7

![Page Image](https://bcdn.docswell.com/page/GE8NN1657D.jpg)

Related Work
• Modeless Pinyin-Chinese Input [Chen et al. 2000]
– Convert alphabet (Pinyin) to Chinese
– Using word-surface feature only for classification
• Type-Any [Ehara et al. 2009]
– Convert Alphabet to Any Language
– Need press Delimiter-key when converting
– Using word-surface feature only for classification
7


# Page. 8

![Page Image](https://bcdn.docswell.com/page/LELPPQ937R.jpg)

Approach
-Modeless Japanese Input Method• Automatically switching input mode
1. Generate discriminating model by Support Vector
Machine (SVM)
– the model describe multiple n-gram features
2. Distinguish a segment whether Kana or not
in alphabet sequences using the discriminating
model
– e.g.
nekohacatdesu → nekoha / cat / desu → ねこはcatです
Japanese / English / Japanese
8


# Page. 9

![Page Image](https://bcdn.docswell.com/page/4JM441ZM7W.jpg)

Main flow of
Modeless Japanese Input Method
User input
(alphabet sequence)
Non Japanese Dic.
Kana-conversion
Discriminative
Model
each character in user inputs
if character is
still ASCII?
True
System Response
(Kana &amp; alphabet sequence)
False
Kana conversion
9


# Page. 10

![Page Image](https://bcdn.docswell.com/page/PJRWWL6L79.jpg)

Flow of
Generating Discriminative Model
Load Texts
• 猫はcatです
Kanji to Kana
• Using Japanese Morphological Analyzer (MeCab)
• ネコハcatデス
Kana to ASCII
• Using Kana to ASCII table (used by Google Japanese input)
• nakohacatdesu
ASCII to n-gram
n-gram to ID
Describe as binary model
Learning on SVM
• character-surface: ne, ek, nek, ko, eko, oh, koh, ha, oha...
• character-type: LL, LL, LLL, LL, LLL, LL, LLL...
• History: KK,KK, KKK, KK, KKK, KKK...
• 1, 3, 4, 13, 22...
• 1:1, 3:1, 4:1, 13:1, 32:1...
• 1.344, 0.691, 0,023, -1.398...
10


# Page. 11

![Page Image](https://bcdn.docswell.com/page/PEXPPWD67X.jpg)

n-gram Features
あ
れ
は
8 B
y t
e
a r e h a 8 B y t e
(in case of n-gram upper limit n = 2, window size m = 2, focus-point xi = 2nd “a”)
• Character-Surface
– Substring of backward and forward at focus point
– e.g.) -2/ha -1/a8 0/8B 1/By
• Character-Type
– Upper-case(U), Lower-case(L), Number(N), and
Symbol(S).
– e.g.) -2/LL -1/LN 0/NU 1/UL
11


# Page. 12

![Page Image](https://bcdn.docswell.com/page/3EKDDVXGJD.jpg)

Generating Non-Japanese Dictionary
• Words never appeared in Japanese only text
– More than 5 length
– Contains substring can’t convert to Kana
• Source
– Corpus of Contemporary American English (COCA)
– Japanese Wikipedia article title list
12


# Page. 13

![Page Image](https://bcdn.docswell.com/page/L73RRM55J5.jpg)

Compare with Conventional IME
Conventional method
areha [Return][Alphabet Mode] 8Byte [Japanese Mode] desu
Switching
Switching
Typing : 17
Modeless Japanese input method
areha8Bytedesu
Typing : 14
• The number of typing key is decreased
13


# Page. 14

![Page Image](https://bcdn.docswell.com/page/87DYY6DY7G.jpg)

Datasets
used in Evaluation Experiment
• Generating Model &amp; Evaluating Method
– Balanced Corpus of Contemporary Written
Japanese (BCCWJ)
• book, magazine, blog, government document and
others
• Non Japanese Dictionary Source
– COCA
– Japanese Wikipedia article title list
14


# Page. 15

![Page Image](https://bcdn.docswell.com/page/VJP66YX2E8.jpg)

Criteria


# Page. 16

![Page Image](https://bcdn.docswell.com/page/2EV33GLX7Q.jpg)

Results of Evaluation
Baseline
(Char. surface
n-gram)
Proposed method
(Char. {surface, type}
n-gram &amp; Dictionary)
Kana Precision
.998
.999
ASCII Precision
.989
.996
Kana Recall
.993
.780
.998
.884
.953
.858
.968
.924
ASCII Recall
Kana F1-measure
ASCII F1-measure
• Outperforms baseline
16


# Page. 17

![Page Image](https://bcdn.docswell.com/page/57G338XRJL.jpg)

User test
• 4 females and 7 males
• Input example sentences (chat, mail, technological
text)
Person No.
1
2
3
4
5
6
7
8
9
Conventional
18.18 17.89 15.4
IME
12.71 11.09 10.18 11.42 12.38 10.48
Proposed
method
12.23 6.03
13.34 14.68 9.88
7.00
…
11.03 11.37 10.30
• Outperforms conventional method
17


# Page. 18

![Page Image](https://bcdn.docswell.com/page/4EQ5598YJP.jpg)

Summary
• Switching input mode is cumbersome
• Hybrid Modeless Japanese Input Method
– Automatically switching input mode between
Japanese and ASCII
– Using n-gram features model for discrimination
• character-{surface, type}
– Outperforms conventional methods
18


