TY - JOUR
T1 - Exploiting word positional information in ngram model for Chinese text input method
AU - 
JO - Journal of Information and Computing Science
VL - 3
SP - 215
EP - 222
PY - 2024
DA - 2024/01
SN - 2
DO - http://doi.org/
UR - https://global-sci.org/intro/article_detail/jics/22801.html
KW -  ngram  model,  stationary  hypothesis,  Pinyin-to-Character  Conversion,  Chinese  text  input 
method 

AB -  This  paper  aims  to  improve  the  performance  of  the  Pinyin-to-Character  Conversion  system 
which  is  the  core  of  Chinese  text  input  method.  The  ngram  model  is  the  current  solution  to  the  Pinyin-to-
Character  Conversion  system.  This  paper  enhances  the  traditional  ngram  model  by  relaxing  its  stationary 
hypothesis and exploiting the word positional information. The Non-stationary ngram (NS ngram) model is 
proposed. Several related issues are discussed in detail, including the formal definition, the model implement, 
the  training  algorithm  and  the  space  complexity  of  the  NS  ngram  model.  Evaluated  on  the  Pinyin-to-
Character Conversion task, the NS ngram model outperforms the traditional ngram model significantly with 
great  error  rate  reductions.  Meanwhile,  the  training  algorithm  presented  in  this  paper  can  estimate  the 
parameters in the NS ngram model effectively and efficiently.