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A package to use chinese word net to achieve word sense disambigution task

Project description

Word Sense Disambiguaion by Chinese Word Net

Chinese word sense disambiguation has been known to a very difficult problem since Chinese is a complicated language. A word can have dozens or even hundreds of meanings on different occasions. Manually labels the senses of the words is labor-intensive and inefficient.

In this project, we aim to solve this problem by state-of-the-art Bert model. It gives us huge performance gains and can score roughly 82% accuracy on Chinese word sense disambiguation problem.


  • Input should be tokenized first. POS Tagging is preferred but not required.
  • Suppose we have m sentences and each sentence has $n_m$ words.
    • list_of_sentence[ [list_of_word[[target, pos, sense_id, sense] * $n_m$ ] *m ]

    • The following is an example that has 2 sentences, input data should be formed as following


How to get sense

  • At Project root directory (same as

      pip3 install .
      import CWN_WSD
      data = read_somewhere() #list of sentence, and sentence is composed as list of word
      sense = CWN_WSD.wsd(data)
  • example can be found under example folder


We thank Po-Wen Chen ( and Yu-Yu Wu ( for contributions in model development.

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