我有一个很大的示例文本，例如:
"The arterial high blood pressure may engage the prognosis for survival of the patient as a result of complications. TENSTATEN enters within the framework of a preventive treatment(processing). His(Her,Its) report(relationship) efficiency / effects unwanted is important. diuretics, medicine of first intention of which TENSTATEN, is. The therapeutic alternatives are very numerous."
我正在尝试检测文本中是否有“参与生存预后”，但是以模糊的方式。例如“具有生存预后”也必须返回肯定的答案。
我研究了 fuzzywuzzy、nltk 和新的正则表达式模糊函数，但我没有找到方法:
if [anything similar (>90%) to "that sentence"] in mybigtext:
print True
最佳答案
以下内容并不理想，但应该可以帮助您入门。它使用 nltk 首先将文本拆分为单词，然后生成一个包含所有单词词干的集合，过滤掉任何停用词。它对示例文本和示例查询都执行此操作。
如果两个集合的交集包含查询中的所有单词，则视为匹配。
import nltk
from nltk.stem import PorterStemmer
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
stop_words = stopwords.words('english')
ps = PorterStemmer()
def get_word_set(text):
return set(ps.stem(word) for word in word_tokenize(text) if word not in stop_words)
text1 = "The arterial high blood pressure may engage the prognosis for survival of the patient as a result of complications. TENSTATEN enters within the framework of a preventive treatment(processing). His(Her,Its) report(relationship) efficiency / effects unwanted is important. diuretics, medicine of first intention of which TENSTATEN, is. The therapeutic alternatives are very numerous."
text2 = "The arterial high blood pressure may engage the for survival of the patient as a result of complications. TENSTATEN enters within the framework of a preventive treatment(processing). His(Her,Its) report(relationship) efficiency / effects unwanted is important. diuretics, medicine of first intention of which TENSTATEN, is. The therapeutic alternatives are very numerous."
query = "engage the prognosis for survival"
set_query = get_word_set(query)
for text in [text1, text2]:
set_text = get_word_set(text)
intersection = set_query & set_text
print "Query:", set_query
print "Test:", set_text
print "Intersection:", intersection
print "Match:", len(intersection) == len(set_query)
print
该脚本提供了两个文本，一个通过，另一个未通过，它会生成以下输出来向您展示它正在做什么:
Query: set([u'prognosi', u'engag', u'surviv'])
Test: set([u'medicin', u'prevent', u'effici', u'engag', u'Her', u'process', u'within', u'surviv', u'high', u'pressur', u'result', u'framework', u'diuret', u')', u'(', u',', u'/', u'.', u'numer', u'Hi', u'treatment', u'import', u'complic', u'altern', u'patient', u'relationship', u'may', u'arteri', u'effect', u'prognosi', u'intent', u'blood', u'report', u'The', u'TENSTATEN', u'unwant', u'It', u'therapeut', u'enter', u'first'])
Intersection: set([u'prognosi', u'engag', u'surviv'])
Match: True
Query: set([u'prognosi', u'engag', u'surviv'])
Test: set([u'medicin', u'prevent', u'effici', u'engag', u'Her', u'process', u'within', u'surviv', u'high', u'pressur', u'result', u'diuret', u')', u'(', u',', u'/', u'.', u'numer', u'Hi', u'treatment', u'import', u'complic', u'altern', u'patient', u'relationship', u'may', u'arteri', u'effect', u'framework', u'intent', u'blood', u'report', u'The', u'TENSTATEN', u'unwant', u'It', u'therapeut', u'enter', u'first'])
Intersection: set([u'engag', u'surviv'])
Match: False
关于python - 模糊搜索Python，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/35705582/