如何使用python（nltk）匹配段落中的关键字

Question

关键词：

Keywords={u'secondary': [u'sales growth', u'next generation store', u'Steps Down', u' Profit warning', u'Store Of The Future', u'groceries']}

段落：

paragraph="""HOUSTON -- Target has unveiled its first "next generation" store in the Houston area, part of a multibillion-dollar effort to reimagine more than 1,000 stores nationwide to compete with e-commerce giants.

The 124,000-square-foot store, which opened earlier this week at Aliana market center in Richmond, Texas, has two distinct entrances and aims to appeal to consumers on both ends of the shopping spectrum.

Busy families seeking convenience can enter the "ease" side of the store, which offers a supermarket-style experience. Customers can pick up online orders, both in store and curbside, and buy grab-and-go items like groceries, wine, last-minute gifts, cleaning supplies and prepared meals."""

有没有办法匹配段落中的关键字？（不使用正则表达式）

输出：

匹配关键词：下一代商店，杂货

Answer 1

无需为此使用 NLTK。 首先，您必须清理段落中的文本，或更改“辅助键”列表中的值。 “下一代”商店和“下一代商店”是两个不同的东西。

在此之后，您可以迭代 'secondary' 的值，并检查您的文本中是否存在任何这些字符串。

match = [i for i in Keywords['secondary'] if i in paragraph]

编辑：正如我上面指定的，“下一代”商店和“下一代商店”是两个不同的东西，这就是你只能得到 1 个匹配的原因。 如果你有“下一代商店”和“下一代商店”，你会得到两场比赛——因为实际上有两场比赛。

输入：

paragraph="""HOUSTON -- Target has unveiled its first "next generation" store in the Houston area, part of a multibillion-dollar effort to reimagine more than 1,000 stores nationwide to compete with e-commerce giants.

The 124,000-square-foot store, which opened earlier this week at Aliana market center in Richmond, Texas, has two distinct entrances and aims to appeal to consumers on both ends of the shopping spectrum.

Busy families seeking convenience can enter the "ease" side of the store, which offers a supermarket-style experience. Customers can pick up online orders, both in store and curbside, and buy grab-and-go items like groceries, wine, last-minute gifts, cleaning supplies and prepared meals."""

输出：

['groceries']

输入：

paragraph="""HOUSTON -- Target has unveiled its first next generation store in the Houston area, part of a multibillion-dollar effort to reimagine more than 1,000 stores nationwide to compete with e-commerce giants.

The 124,000-square-foot store, which opened earlier this week at Aliana market center in Richmond, Texas, has two distinct entrances and aims to appeal to consumers on both ends of the shopping spectrum.

Busy families seeking convenience can enter the "ease" side of the store, which offers a supermarket-style experience. Customers can pick up online orders, both in store and curbside, and buy grab-and-go items like groceries, wine, last-minute gifts, cleaning supplies and prepared meals."""

输出：

['next generation store','groceries']

Answer 2

首先，如果您的关键字只有一个键，则您实际上并不需要dict 。 使用set()代替。

Keywords={u'secondary': [u'sales growth', u'next generation store', 
                         u'Steps Down', u' Profit warning', 
                         u'Store Of The Future', u'groceries']}

keywords = {u'sales growth', u'next generation store', 
            u'Steps Down', u' Profit warning', 
            u'Store Of The Future', u'groceries'}

paragraph="""HOUSTON -- Target has unveiled its first "next generation" store in the Houston area, part of a multibillion-dollar effort to reimagine more than 1,000 stores nationwide to compete with e-commerce giants.
The 124,000-square-foot store, which opened earlier this week at Aliana market center in Richmond, Texas, has two distinct entrances and aims to appeal to consumers on both ends of the shopping spectrum.
Busy families seeking convenience can enter the "ease" side of the store, which offers a supermarket-style experience. Customers can pick up online orders, both in store and curbside, and buy grab-and-go items like groceries, wine, last-minute gifts, cleaning supplies and prepared meals."""

然后对 Python 中标记化文本中的 Find multi-word term 做一个小调整

from nltk.tokenize import MWETokenizer
from nltk import sent_tokenize, word_tokenize

mwe = MWETokenizer([k.lower().split() for k in keywords], separator='_')

# Clean out the punctuations in your sentence.
import string
puncts = list(string.punctuation)
cleaned_paragraph = ''.join([ch if ch not in puncts else '' for ch in paragraph.lower()])

tokenized_paragraph = [token for token in mwe.tokenize(word_tokenize(cleaned_paragraph))
                       if token.replace('_', ' ') in keywords]

print(tokenized_paragraph)

[出去]：

>>> print(tokenized_paragraph)
['next_generation_store', 'groceries'