在Python 3.3.2中計算詞組頻率

Question

我一直在研究Web上的不同來源，並嘗試了各種方法，但只能找到如何計算唯一單詞而不是唯一短語的頻率。 到目前為止，我的代碼如下：

import collections
import re
wanted = set(['inflation', 'gold', 'bank'])
cnt = collections.Counter()
words = re.findall('\w+', open('02.2003.BenBernanke.txt').read().lower())
for word in words:
    if word in wanted:
        cnt [word] += 1
print (cnt)

如果可能的話，我還要統計一下本文中使用“中央銀行”和“高通貨膨脹”這一短語的次數。 感謝您提供的任何建議或指導。

Answer 1

首先，這就是我將如何生成您執行的cnt （以減少內存開銷）的方式

def findWords(filepath):
  with open(filepath) as infile:
    for line in infile:
      words = re.findall('\w+', line.lower())
      yield from words

cnt = collections.Counter(findWords('02.2003.BenBernanke.txt'))

現在，關於您的短語問題：

from itertools import tee
phrases = {'central bank', 'high inflation'}
fw1, fw2 = tee(findWords('02.2003.BenBernanke.txt'))   
next(fw2)
for w1,w2 in zip(fw1, fw2)):
  phrase = ' '.join([w1, w2])
  if phrase in phrases:
    cnt[phrase] += 1

希望這可以幫助

Answer 2

假設文件不是很大-這是最簡單的方法

for w1, w2 in zip(words, words[1:]):
    phrase = w1 + " " + w2
    if phrase in wanted:
        cnt[phrase] += 1
print(cnt)

Answer 3

要在一個小文件中計算幾個短語的字面出現次數：

with open("input_text.txt") as file:
    text = file.read()
n = text.count("high inflation rate")

nltk.collocations模塊提供了一些工具來識別經常連續出現的單詞：

import nltk
from nltk.tokenize import word_tokenize, sent_tokenize
from nltk.collocations import BigramCollocationFinder, TrigramCollocationFinder

# run nltk.download() if there are files missing
words = [word.casefold() for sentence in sent_tokenize(text)
         for word in word_tokenize(sentence)]
words_fd = nltk.FreqDist(words)
bigram_fd = nltk.FreqDist(nltk.bigrams(words))
finder = BigramCollocationFinder(word_fd, bigram_fd)
bigram_measures = nltk.collocations.BigramAssocMeasures()
print(finder.nbest(bigram_measures.pmi, 5))
print(finder.score_ngrams(bigram_measures.raw_freq))

# finder can be constructed from words directly
finder = TrigramCollocationFinder.from_words(words)
# filter words
finder.apply_word_filter(lambda w: w not in wanted)
# top n results
trigram_measures = nltk.collocations.TrigramAssocMeasures()
print(sorted(finder.nbest(trigram_measures.raw_freq, 2)))