Question

我有以下示例标记化数据框：

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']

我成功运行了以下代码以找出ngram短语。

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

# only bigrams that appear 1+ times
finder.apply_freq_filter(1) 

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

结果显示如下，前10个pmi：

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

我希望以上结果出现在一个包含频率计数的数据帧中，该频率计数显示了这些二元数据发生的频率。

采样所需的输出：

ngram                    frequency
'brewing', 'properly'    1
'galley', 'work'         1
'maker', 'brewing'       1
'properly', '2'          1
...                      ...

如何在Python中执行上述操作？

Answer 1

这应该做...

首先，设置您的数据集（或类似数据集）：

import pandas as pd
from nltk.collocations import *
import nltk.collocations
from nltk import ngrams
from collections import Counter

s = pd.Series(
    [
        ['coffee', 'maker', 'brewing', 'properly', '2', '420', '420', '420'],
        ['galley', 'work', 'table', 'stuck'],
        ['cloth', 'stuck'],
        ['stuck', 'coffee']
    ]
)

finder = BigramCollocationFinder.from_documents(s.values)
bigram_measures = nltk.collocations.BigramAssocMeasures()

# only bigrams that appear 1+ times
finder.apply_freq_filter(1) 

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

使用nltk.ngrams重新创建ngram列表：

ngram_list = [pair for row in s for pair in ngrams(row, 2)]

使用collections.Counter来计算每个ngram在整个语料库中出现的次数：

counts = Counter(ngram_list).most_common()

构建一个看起来像您想要的数据框：

pd.DataFrame.from_records(counts, columns=['gram', 'count'])
                   gram  count
0            (420, 420)      2
1       (coffee, maker)      1
2      (maker, brewing)      1
3   (brewing, properly)      1
4         (properly, 2)      1
5              (2, 420)      1
6        (galley, work)      1
7         (work, table)      1
8        (table, stuck)      1
9        (cloth, stuck)      1
10      (stuck, coffee)      1

然后，您可以过滤以仅查看由finder.nbest调用产生的ngram：

df = pd.DataFrame.from_records(counts, columns=['gram', 'count'])
df[df['gram'].isin(result)]

Python Pandas NLTK：使用BigramCollocationFinder从数据框中的文本字段显示常用短语（ngram）的频率

1 个答案: