如何通过nltk.pos_tag（）函数使用通用POS标签？

Question

我有一个文本，我想找到'ADJ'，'PRON'，'VERB'，'NOUN'等的数量。我知道有.pos_tag()函数，但是它给我不同的结果，并且我希望得到的结果为'ADJ'，'PRON'，'VERB'，'NOUN'。这是我的代码：

import nltk
from nltk.corpus import state_union, brown
from nltk.corpus import stopwords
from nltk import ne_chunk

from nltk.tokenize import PunktSentenceTokenizer
from nltk.tokenize import word_tokenize
from nltk.tokenize import RegexpTokenizer
from nltk.stem import WordNetLemmatizer 

from collections import Counter

sentence = "this is my sample text that I want to analyze with programming language"

# tokenizing text (make list with evey word)
sample_tokenization = word_tokenize(sample)
print("THIS IS TOKENIZED SAMPLE TEXT, LIST OF WORDS:\n\n", sample_tokenization)
print()

# tagging words
taged_words = nltk.pos_tag(sample_tokenization.split(' '))
print(taged_words)
print()


# showing the count of every type of word for new text
count_of_word_type = Counter(word_type for word,word_type in taged_words)
count_of_word_type_list = count_of_word_type.most_common() # making a list of tuples counts
print(count_of_word_type_list)


for w_type, num in count_of_word_type_list:
     print(w_type, num)
print() 

上面的代码有效，但是我想找到一种获取这种类型的标签的方法：

Tag Meaning English Examples
ADJ adjective   new, good, high, special, big, local
ADP adposition  on, of, at, with, by, into, under
ADV adverb  really, already, still, early, now
CONJ    conjunction and, or, but, if, while, although
DET determiner, article the, a, some, most, every, no, which
NOUN    noun    year, home, costs, time, Africa
NUM numeral twenty-four, fourth, 1991, 14:24
PRT particle    at, on, out, over per, that, up, with
PRON    pronoun he, their, her, its, my, I, us
VERB    verb    is, say, told, given, playing, would
.   punctuation marks   . , ; !
X   other   ersatz, esprit, dunno, gr8, univeristy

我看到这里有一章：https://www.nltk.org/book/ch05.html

说：

from nltk.corpus import brown
brown_news_tagged = brown.tagged_words(categories='news', tagset='universal')

但是我不知道如何将其应用于我的例句。感谢您的帮助。

Answer 1

来自https://github.com/nltk/nltk/blob/develop/nltk/tag/init.py#L135

>>> from nltk.tag import pos_tag
>>> from nltk.tokenize import word_tokenize

# Default Penntreebank tagset.
>>> pos_tag(word_tokenize("John's big idea isn't all that bad."))
[('John', 'NNP'), ("'s", 'POS'), ('big', 'JJ'), ('idea', 'NN'), ('is', 'VBZ'),
("n't", 'RB'), ('all', 'PDT'), ('that', 'DT'), ('bad', 'JJ'), ('.', '.')]

# Universal POS tags.
>>> pos_tag(word_tokenize("John's big idea isn't all that bad."), tagset='universal')
[('John', 'NOUN'), ("'s", 'PRT'), ('big', 'ADJ'), ('idea', 'NOUN'), ('is', 'VERB'),
("n't", 'ADV'), ('all', 'DET'), ('that', 'DET'), ('bad', 'ADJ'), ('.', '.')]