改进 Python 中的模糊匹配算法

Question

任务：获取两个文本文件并输出 100% 匹配和 75% 匹配。

解决方案：

import difflib
import csv

# Imports and parses the files
fileA = open("H:/comm.names.txt", 'r')
try:
        setA = fileA.readlines()
finally:    
        fileA.close()

fileB = open("H:/acad.names.txt", 'r')
try:
        setB = fileB.readlines()
finally:    
        fileB.close()

# 100% Match
setMatch100 = set(setA).intersection(setB)

Match100 = open("H:\Match100.txt", 'w')
try:
    for item in setMatch100:
        Match100.write(item)
finally:
    Match100.close()

# Remove 100% matches from the two lists
setA_LeftOver = set(setA).difference(setMatch100)
setB_LeftOver = set(setB).difference(setMatch100)

#Return the best match for setA_LeftOver[i] in setB_LeftOver that is at least 75% matching.
fMatch75 = open("H:\Match75.csv", 'w')
Match75 = csv.writer(fMatch75)
try:
    Match75.writerow(['File A', 'File B'])
    for item in setA_LeftOver:
                match = difflib.get_close_matches(item, setB_LeftOver, 1, 0.75)
                if len(match) > 0:
                        row = [item.rstrip(), match[0].rstrip()]
                        Match75.writerow(row)


finally:
    fMatch75.close()

问题：这可行，但结果不是很好。下面是一个匹配的例子：

Fovea Pharmaceuticals SA Kobe Pharmaceutical Univ

I can't turn up the minimum percent in Diff by too much because I need to be able to match Univ with University. Also, I can't just make sure that the first words match because some strings start with "The" and need to be matched with strings that exclude "The". Can anyone point me in a direction that would throw out matches that technically are 75% similar, but to a human aren't similar at all?

Answer 1

我最终写了一个最常见的单词脚本，然后删除了最常见的单词。正如 @e-satis 在他的评论中建议的那样，这显着改善了我的结果。然而，difflib 给了我比 pylevenshtein 更好的结果，所以我不能将他的答案标记为已接受。

Answer 2

我会尝试使用 pylevenshtein 等工具比较字符串。它允许模糊字符串比较。