如何在迭代三个匹配单词的列表后创建 Python 字典

mlds522

我需要在迭代三个 LIST 后创建一个字典。用于匹配句子(list_sent as KEYs)和词列表(list_wordset as VALUEs)用于匹配关键字(list_keywords)。请参阅下面的列表和预期输出字典以及解释。请建议。

list_sent = ['one more shock like Covid-19',
             'The number of people suffering acute',
             'people must collectively act now',
             'handling the novel coronavirus outbreak',
              'After a three-week nationwide',
              'strengthening medical quarantine']

list_wordset = [['people','suffering','acute'],
                ['Covid-19','Corona','like'], 
                ['people','jersy','country'],
                ['novel', 'coronavirus', 'outbreak']]

list_keywords = ['people', 'Covid-19', 'nationwide','quarantine','handling']

'Covid-19' 关键字出现在 list_sent 和 list_wordset 中,因此它们会在 Dictionary 中捕获。'people' 关键字出现在 list_sent 中的 2 个不同项目和 list_wordset 中的 2 个不同列表中,因此需要捕获它们。即使 list_wordset 中的单个单词与关键字匹配,那么它也是匹配的。

预期输出为:

out_dict =

{'one more shock like Covid-19': ['Covid-19','Corona','like'],
 'The number of people suffering acute': [['people','suffering','acute'],['people','jersy','country']],
 'people must collectively act now' : [['people','suffering','acute'],['people','jersy','country']]}
mlds522

我能够使用所有 3 个列表,以字典格式创建所需的输出。要删除空值,请使用附加步骤。

out_dict = {sent: [wordset for wordset in list_wordset if any(key in sent and key in wordset for key in list_keywords)]
 for sent in list_sent}

结果:

{'one more shock like Covid-19': [['Covid-19', 'Corona', 'like']],
 'The number of people suffering acute': [['people', 'suffering', 'acute'],
  ['people', 'jersy', 'country']],
 'people must collectively act now': [['people', 'suffering', 'acute'],
  ['people', 'jersy', 'country']],
 'handling the novel coronavirus outbreak': [],
 'After a three-week nationwide': [],
 'strengthening medical quarantine': []}

要删除空列表值:

out_dict = dict( [(k,v) for k,v in out_dict.items() if len(v)>0])

最终结果:

{'one more shock like Covid-19': [['Covid-19', 'Corona', 'like']],
 'The number of people suffering acute': [['people', 'suffering', 'acute'],
  ['people', 'jersy', 'country']],
 'people must collectively act now': [['people', 'suffering', 'acute'],
  ['people', 'jersy', 'country']]}

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