python - Python Pandas NLTK从数据框“join()参数”的文本字段中提取通用短语(ngram)错误

我有以下示例数据框：

No  category    problem_definition_stopwords
175 2521       ['coffee', 'maker', 'brewing', 'properly', '2', '420', '420', '420']
211 1438       ['galley', 'work', 'table', 'stuck']
912 2698       ['cloth', 'stuck']
572 2521       ['stuck', 'coffee']

“problem\u definition\u stopwords”字段已标记为删除了stop-gap单词。
我想从“problem\u definition\u stopwords”字段创建n个grams。具体来说，我想从我的数据中提取n个图，并找到具有最高点互信息（PMI）的图。
从本质上说，我想找到共同出现的词，比我希望它们偶然出现的词要多得多。
我尝试了以下代码：

import nltk
from nltk.collocations import *

bigram_measures = nltk.collocations.BigramAssocMeasures()
trigram_measures = nltk.collocations.TrigramAssocMeasures()

# errored out here
finder = BigramCollocationFinder.from_words(nltk.corpus.genesis.words(df['problem_definition_stopwords']))

# only bigrams that appear 3+ times
finder.apply_freq_filter(3)

# return the 10 n-grams with the highest PMI
finder.nbest(bigram_measures.pmi, 10)

我收到的错误是在第三段代码上。。。
TypeError:join（）参数必须是str或bytes，而不是'list'
编辑：数据帧的更可移植格式：

>>> df.columns
Index(['No', 'category', 'problem_definition_stopwords'], dtype='object')
>>> df.to_dict()
{'No': {0: 175, 1: 211, 2: 912, 3: 572}, 'category': {0: 2521, 1: 1438, 2: 2698, 3: 2521}, 'problem_definition_stopwords': {0: ['coffee', 'maker', 'brewing', 'properly', '2', '420', '420', '420'], 1: ['galley', 'work', 'table', 'stuck'], 2: ['cloth', 'stuck'], 3: ['stuck', 'coffee']}}

最佳答案

看起来你使用from_words调用的方式不对，看help(nltk.corpus.genesis.words)

Help on method words in module nltk.corpus.reader.plaintext:

words(fileids=None) method of nltk.corpus.reader.plaintext.PlaintextCorpusReader instance
    :return: the given file(s) as a list of words
        and punctuation symbols.
    :rtype: list(str)
(END)

这就是你要找的吗？因为您已经将文档表示为字符串列表，在我的经验中，它与NLTK很好地配合，所以我认为您可以使用from_documents方法：

finder = BigramCollocationFinder.from_documents(
    df['problem_definition_stopwords']
)

# only bigrams that appear 3+ times
# Note, I limited this to 1 since the corpus you provided
# is very small and it'll be tough to find repeat ngrams
finder.apply_freq_filter(1)

# return the 10 n-grams with the highest PMI
finder.nbest(bigram_measures.pmi, 10)

[('brewing', 'properly'), ('galley', 'work'), ('maker', 'brewing'), ('properly', '2'), ('work', 'table'), ('coffee', 'maker'), ('2', '420'), ('cloth', 'stuck'), ('table', 'stuck'), ('420', '420')]

关于python - Python Pandas NLTK从数据框“join()参数”的文本字段中提取通用短语(ngram)错误，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/53560439/