fuzzychinese #

形近词中文模糊匹配

A simple tool to fuzzy match chinese words, particular useful for proper name matching and address matching.

一个可以模糊匹配形近字词的小工具。对于专有名词，地址的匹配尤其有用。

安装说明 #

pip install fuzzychinese

使用说明 #

首先使用想要匹配的字典对模型进行训练。

然后用FuzzyChineseMatch.transform(raw_words, n) 来快速查找与raw_words的词最相近的前n个词。

训练模型时有三种分析方式可以选择，笔划分析(stroke)，部首分析(radical)，和单字分析(char)。也可以通过调整ngram_range的值来提高模型性能。

匹配完成后返回相似度分数，匹配的相近词语及其原有索引号。

    import pandas as pd
    from fuzzychinese import FuzzyChineseMatch
    test_dict =  pd.Series(['长白朝鲜族自治县','长阳土家族自治县','城步苗族自治县','达尔罕茂明安联合旗','汨罗市'])
    raw_word = pd.Series(['达茂联合旗','长阳县','汩罗市'])
    assert('汩罗市'!='汨罗市') # They are not the same!

    fcm = FuzzyChineseMatch(ngram_range=(3, 3), analyzer='stroke')
    fcm.fit(test_dict)
    top2_similar = fcm.transform(raw_word, n=2)
    res = pd.concat([
        raw_word,
        pd.DataFrame(top2_similar, columns=['top1', 'top2']),
        pd.DataFrame(
            fcm.get_similarity_score(),
            columns=['top1_score', 'top2_score']),
        pd.DataFrame(
            fcm.get_index(),
            columns=['top1_index', 'top2_index'])],
                    axis=1)

其他功能 #

• 直接使用Stroke, Radical进行汉字分解。

  stroke = Stroke()
  radical = Radical()
  print("像", stroke.get_stroke("像"))
  print("像", radical.get_radical("像"))
  

  像 ㇒〡㇒㇇〡㇕一㇒㇁㇒㇒㇒㇏
  像 人象
  

• 使用FuzzyChineseMatch.compare_two_columns(X, Y)对每一行的两个词进行比较，获得相似度分数。

• 详情请参见说明文档.

致谢 #

拆字数据来自于 漢語拆字字典 by 開放詞典網。

Installation #

pip install fuzzychinese

Quickstart #

First train a model with the target list of words you want to match to.

Then use FuzzyChineseMatch.transform(raw_words, n) to find top n most similar words in the target for your raw_words .

There are three analyzers to choose from when training a model: stroke, radical, and char. You can also change ngram_range to fine-tune the model.

After the matching, similarity score, matched words and its corresponding index are returned.

    from fuzzychinese import FuzzyChineseMatch
    test_dict =  pd.Series(['长白朝鲜族自治县','长阳土家族自治县','城步苗族自治县','达尔罕茂明安联合旗','汨罗市'])
    raw_word = pd.Series(['达茂联合旗','长阳县','汩罗市'])
    assert('汩罗市'!='汨罗市') # They are not the same!

    fcm = FuzzyChineseMatch(ngram_range=(3, 3), analyzer='stroke')
    fcm.fit(test_dict)
    top2_similar = fcm.transform(raw_word, n=2)
    res = pd.concat([
        raw_word,
        pd.DataFrame(top2_similar, columns=['top1', 'top2']),
        pd.DataFrame(
            fcm.get_similarity_score(),
            columns=['top1_score', 'top2_score']),
        pd.DataFrame(
            fcm.get_index(),
            columns=['top1_index', 'top2_index'])],
                    axis=1)

Other use #

• Directly use Stroke, Radical to decompose Chinese character into strokes or radicals.

  stroke = Stroke()
  radical = Radical()
  print("像", stroke.get_stroke("像"))
  print("像", radical.get_radical("像"))
  

  像 ㇒〡㇒㇇〡㇕一㇒㇁㇒㇒㇒㇏
  像 人象
  

• Use FuzzyChineseMatch.compare_two_columns(X, Y) to compare the pair of words in each row to get similarity score.

• See documentation for details.

Credits #

Data for Chinese radicals are from 漢語拆字字典 by 開放詞典網.