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g2pC: A Context-aware Grapheme-to-Phoneme Conversion module for Chinese
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g2pc v0.9.9.3 Jul 10, 2019
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README.md Added performance comparison Jul 10, 2019
setup.py v0.9.9.3 Jul 10, 2019

README.md

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g2pC: A Context-aware Grapheme-to-Phoneme for Chinese

There are several open source libraries of Chinese grapheme-to-phoneme conversion such as python-pinyin or xpinyin. However, none of them seem to disambiguate Chinese polyphonic words like "行" ("xíng" (go, walk) vs. "háng" (line)) or "了" ("le" (completed action marker) vs. "liǎo" (finish, achieve)). Instead, they pick up the most frequent pronunciation. Although that may be a simple and economic strategy, machine learning techniques can be of help. We use CRF to determine the pronunciation of polyphonic words. In addition to the target word itself and its part-of-speech, which are tagged by pkuseg, its neighboring words are also featurized.

Requirements

  • python >= 3.6
  • pkuseg
  • sklearn_crfsuite

Installation

pip install g2pc

Main Features

  • Disambiguate polyphonic Chinese characters/words and return the most likely pinyin in the context using CRF implemented with sklearn_crfsuite.
  • By associating segmentation results provided by pkuseg with an open-source dictionary CC-CEDICT, display the following comprehensive information.
    • word
    • part-of-speech
    • pinyin
    • descriptive pinyin: where Chinese tone change rules are applied
    • English meaning
    • traditional equivalent

Algorithm (illustrated with an example)

e.g., Input: 我写了几行代码。 (I wrote a few lines of codes.)

  • STEP 1. Segment input string using pkuseg.

    • -> [('我', 'r'), ('写', 'v'), ('了', 'u'), ('几', 'm'), ('行', 'q'), ('代码', 'n'), ('。', 'w')]
  • STEP 2. Look up the CC-CEDICT. Each token, a tuple, consists of word, pos, pronunciation candidates, meaning candidates, traditional character candidates.

    • -> [('我', 'r', ['wo3'], ['/I/me/my/'], ['我']),
      ('写', 'v', ['xie3'], ['/to write/'], ['寫']),
      ('了', 'u', ['le5', 'liao3', 'liao4'], [dal particle ..], ['了', '了', '瞭']),
      ('几', 'm', ['ji3', 'ji1'], ['/how much/..'], ['幾', '几']),
      ('行', 'q', ['xing2', 'hang2'], ['/to walk/.."], ['行', '行']),
      ('代码', 'n', ['dai4 ma3'], ['/code/'], ['代碼']),
      ('。', 'w', ['。'], [''], ['。'])]
  • STEP 3. For polyphonic words, we disambiguate them, using our pre-trained CRF model.

    • -> [('我', 'r', 'wo3', '/I/me/my/', '我'),
      ('写', 'v', 'xie3', '/to write/', '寫'),
      ('了', 'u', 'le5', '/(modal particle ..', '了'),
      ('几', 'm', 'ji3', '/how much/..', '幾'),
      ('行', 'q', 'hang2', "/row/..", '行'),
      ('代码', 'n', 'dai4 ma3', '/code/', '代碼'),
      ('。', 'w', '。', '。', '', '。')]
  • STEP 4. Tone change rules are applied.

    • -> [('我', 'r', 'wo3', 'wo2', '/I/me/my/', '我'),
      ('写', 'v', 'xie3', 'xie3', '/to write/', '寫'),
      ('了', 'u', 'le5', 'le5', '/(modal particle ..', '了'),
      ('几', 'm', 'ji3', 'ji3', '/how much/..', '幾'),
      ('行', 'q', 'hang2', 'hang2, "/row/..", '行'),
      ('代码', 'n', 'dai4 ma3', 'dai4 ma3', '/code/', '代碼'),
      ('。', 'w', '。', '。', '', '。')]

Usage

>>> from g2pc import G2pC
>>> g2p = G2pC()
>>> g2p("一心一意")
# This returns a list of tuples, each of which consists of
# word, pos, pinyin, (tone changed) descriptive pinyin, English meaning, and equivanlent traditional character.
[[('一心一意', 
'i', 
'yi1 xin1 yi1 yi4', 
'yi4 xin1 yi2 yi4', 
"/concentrating one's thoughts and efforts/single-minded/bent on/intently/", 
'一心一意')]

Respectful comparison with other libraries

>>> text1 = "我写了几行代码。" # pay attention to the 行, which should be read as 'hang2', not 'xing2'
>>> text2 = "来不了" # pay attention to the 了, which should be read as 'liao3', not 'le'
# python-pinyin
>>> pip install pypinyin
>>> from pypinyin import pinyin
>>> pinyin(text1)
[['wǒ'], ['xiě'], ['le'], ['jǐ'], ['xíng'], ['dài'], ['mǎ'], ['。']]
>>> pinyin(text2)
[['lái'], ['bù'], ['le']]
# xpinyin
>>> pip install xpinyin
>>> from xpinyin import Pinyin
>>> p = Pinyin()
>>> p.get_pinyin(text1, tone_marks="numbers")  
'wo3-xie3-le5-ji1-xing2-dai4-ma3-。'
>>> p.get_pinyin(text2, tone_marks="numbers")   
'lai2-bu4-le5'
  • Accuracy on internal test set (13,191 syllables)
Model # Correct # Incorrect Acc. (%)
g2pC (0.9.9.3) 13,033 158 98.80
pypinyin (0.35.3) 12,975 216 98.36
xpinyin (0.5.6) 12,838 353 97.32

Accuracy

Changelog

0.9.9.3 July 10, 2019

  • Refined the tone change rules.

0.9.9.2 July 10, 2019

  • Refined the cedict.pkl.

0.9.9.1 July 9, 2019

  • Fixed a bug of failing to find Chinese characters for names. (See this)

0.9.6. July 7, 2019

  • Fixed a bug of failing to converting words not found in the dictionary.
  • Rearragned the cedict.pkl.
  • Refined the CRF model.
  • Added tone change rules. (See this)

0.9.4. July 4, 2019

  • Initial launch

References

If you use our software for research, please cite:

@misc{gp2C2019,
  author = {Park, Kyubyong},
  title = {g2pC},
  year = {2019},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/Kyubyong/g2pC}}
}
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