如何使用 Pyspark 和 NLTK 计算 POS 标签？

问题描述

我有一些文本或一个大文件，我需要使用 NLTK 和 Pyspark 来计算 POS 标签的数量。我找不到导入文本文件的方法，所以我尝试添加一个短字符串但失败了。

计数线需要包含 pyspark。

##textfile = sc.textfile('') 
##or 
##textstring = """This is just a bunch of words to use for this example.  John gave ##them to me last night but Kim took them to work.  Hi Stacy.  ###'''URL:http://example.com'''"""

tstring = sc.parallelize(List(textstring)).collect()

TOKEN_RE = re.compile(r"\b[\w']+\b")

dropURL=text.filter(lambda x: "URL" not in x)

words = dropURL.flatMap(lambda dropURL: dropURL.split(" "))

nltkwords = words.flatMap(lambda words: nltk.tag.pos_tag(nltk.regexp_tokenize(words,TOKEN_RE)))
#word_counts =nltkwords.map(lambda nltkwords: (ntlkwords,1))


nltkwords.take(50)

解决方法

这是您的测试字符串的示例。我认为您只是缺少按空格拆分字符串的步骤。否则整行将被删除，因为 URL 在该行中。

import nltk
import re

textstring = """This is just a bunch of words to use for this example.  John gave ##them to me last night but Kim took them to work.  Hi Stacy.  ###'''URL:http://example.com'''"""

TOKEN_RE = re.compile(r"\b[\w']+\b")
text = sc.parallelize(textstring.split(' '))
dropURL = text.filter(lambda x: "URL" not in x)

words = dropURL.flatMap(lambda dropURL: dropURL.split(" "))

nltkwords = words.flatMap(lambda words: nltk.tag.pos_tag(nltk.regexp_tokenize(words,TOKEN_RE)))

nltkwords.collect()
# [('This','DT'),('is','VBZ'),('just','RB'),('a',('bunch','NN'),('of','IN'),('words','NNS'),('to','TO'),('use',('for',('this',('example',('John','NNP'),('gave','VBD'),('them','PRP'),('me',('last','JJ'),('night',('but','CC'),('Kim',('took',('work',('Hi',('Stacy','NN')]

要计算 pos 标签的出现次数，您可以执行一个 reduceByKey：

word_counts = nltkwords.map(lambda x: (x[1],1)).reduceByKey(lambda x,y: x + y)

word_counts.collect()
# [('NNS',1),('TO',3),('CC',('DT',('JJ',('VBZ',('RB',('NN',7),('VBD',2),('PRP',('IN',('NNP',2)]