Exploiting word positional information in ngram model for Chinese text input method
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@Article{JICS-2-215,
author = {},
title = {Exploiting word positional information in ngram model for Chinese text input method},
journal = {Journal of Information and Computing Science},
year = {2024},
volume = {2},
number = {3},
pages = {215--222},
abstract = { 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.
},
issn = {1746-7659},
doi = {https://doi.org/},
url = {http://global-sci.org/intro/article_detail/jics/22801.html}
}
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.
. (2024). Exploiting word positional information in ngram model for Chinese text input method.
Journal of Information and Computing Science. 2 (3).
215-222.
doi:
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