我有一个文本，我想找到“ADJs”、“PRONs”、“VERBs”、“NOUNs”等的数量。我知道有.pos_tag()功能，但它给了我不同的结果，我想得到“ADJs”的结果'，'代词'，'动词'，'名词'。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，但我想找到一种方法来获取这种类型的标签：
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
说的是：
from nltk.corpus import brown
brown_news_tagged = brown.tagged_words(categories='news', tagset='universal')
但我不知道如何将其应用于我的例句。谢谢你的帮助。