预估校正法python代码
时间: 2023-06-19 22:10:54 浏览: 68
预估校正法(Estimated Correction Method)是一种基于统计学的拼写纠错方法,其思想是根据语料库中每个单词出现的概率来对文本中可能存在的错误单词进行修正。下面是一个简单的预估校正法的Python实现:
```python
import re
from collections import Counter
def words(text):
return re.findall(r'\w+', text.lower())
WORDS = Counter(words(open('big.txt').read()))
def P(word, N=sum(WORDS.values())):
"Probability of `word`."
return WORDS[word] / N
def correction(word):
"Most probable spelling correction for `word`."
return max(candidates(word), key=P)
def candidates(word):
"Generate possible spelling corrections for word."
return (known([word]) or known(edits1(word)) or known(edits2(word)) or [word])
def known(words):
"The subset of `words` that appear in the dictionary of WORDS."
return set(w for w in words if w in WORDS)
def edits1(word):
"All edits that are one edit away from `word`."
letters = 'abcdefghijklmnopqrstuvwxyz'
splits = [(word[:i], word[i:]) for i in range(len(word) + 1)]
deletes = [L + R[1:] for L, R in splits if R]
transposes = [L + R[1] + R[0] + R[2:] for L, R in splits if len(R) > 1]
replaces = [L + c + R[1:] for L, R in splits if R for c in letters]
inserts = [L + c + R for L, R in splits for c in letters]
return set(deletes + transposes + replaces + inserts)
def edits2(word):
"All edits that are two edits away from `word`."
return (e2 for e1 in edits1(word) for e2 in edits1(e1))
```
上述代码中,WORDS是一个Counter对象,其中包含了big.txt文件中所有单词出现的次数。P函数用来计算单词在语料库中出现的概率,correction函数用来修正单词,candidates函数用来产生候选单词列表,known函数用来从候选单词列表中取出出现在语料库中的单词,edits1函数用来产生距离单词编辑距离为1的单词列表,edits2函数用来产生距离单词编辑距离为2的单词列表。