2

我有一个很大的示例文本，例如：

“由于并发症，动脉高血压可能会影响患者的生存预后。TENSTATEN 进入预防性治疗（处理）的框架。他（她，其）报告（关系）不需要的效果/效果很重要. 利尿剂，是 TENSTATEN 的首选药物。治疗替代品非常多。”

而且我试图检测文本中是否“参与生存预后”，但以模糊的方式。例如“已参与生存的预后”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，但我没有找到办法：

if [anything similar (>90%) to "that sentence"] in mybigtext:
    print True
4

3 回答 3

1

使用该regex模块，首先按句子分割，然后测试模糊模式是否在句子中：

tgt="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."

for sentence in regex.split(r'(?<=[.?!;])\s+(?=\p{Lu})', tgt):
    pat=r'(?e)((?:has engage the progronosis of survival){e<%i})' 
    pat=pat % int(len(pat)/5)
    m=regex.search(pat, sentence)
    if m:
        print "'{}'\n\tfuzzy matches\n'{}'\n\twith \n{} substitutions, {} insertions, {} deletions".format(pat,m.group(1), *m.fuzzy_counts)

印刷：

'(?e)((?:has engage the progronosis of survival){e<10})'
    fuzzy matches
'may engage the prognosis for survival'
    with 
3 substitutions, 1 insertions, 2 deletions
于 2016-02-29T21:41:19.050 回答
1

以下内容并不理想，但应该可以帮助您入门。它用于nltk首先将您的文本拆分为单词，然后生成一个包含所有单词词干的集合，过滤任何停用词。它为您的示例文本和示例查询执行此操作。

如果两个集合的交集包含查询中的所有单词，则认为它是匹配的。

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，另一个不通过，它产生以下输出以显示它在做什么：

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
于 2016-02-29T20:32:08.403 回答
0

有一个函数，如果文本中包含一个单词，它将显示匹配项。您可以即兴发挥它来检查文本中的完整短语。

这是我制作的功能：

def FuzzySearch(text, phrase):
    """Check if word in phrase is contained in text"""
    phrases = phrase.split(" ")

    for x in range(len(phrases)):
        if phrases[x] in text:
            print("Match! Found " + phrases[x] + " in text")
        else:
            continue
于 2016-02-29T17:52:16.980 回答