A small package to fuzzy match chinese words 中文模糊匹配
Project description
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)
top1 | top2 | top1_score | top2_score | top1_index | top2_index | |
---|---|---|---|---|---|---|
达茂联合旗 | 达尔罕茂明安联合旗 | 长白朝鲜族自治县 | 0.824751 | 0.287237 | 3 | 0 |
长阳县 | 长阳土家族自治县 | 长白朝鲜族自治县 | 0.610285 | 0.475000 | 1 | 0 |
汩罗市 | 汨罗市 | 长白朝鲜族自治县 | 1.000000 | 0.152093 | 4 | 0 |
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
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)
top1 | top2 | top1_score | top2_score | top1_index | top2_index | |
---|---|---|---|---|---|---|
达茂联合旗 | 达尔罕茂明安联合旗 | 长白朝鲜族自治县 | 0.824751 | 0.287237 | 3 | 0 |
长阳县 | 长阳土家族自治县 | 长白朝鲜族自治县 | 0.610285 | 0.475000 | 1 | 0 |
汩罗市 | 汨罗市 | 长白朝鲜族自治县 | 1.000000 | 0.152093 | 4 | 0 |
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.
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