我认为我的标题的答案通常是去阅读文档，但我浏览了NLTK 书，但它没有给出答案。我对 Python 有点陌生。
我有一堆.txt文件，我希望能够使用 NLTK 为 corpus 提供的语料库功能nltk_data。
我已经尝试过PlaintextCorpusReader，但我无法做到：
>>>import nltk
>>>from nltk.corpus import PlaintextCorpusReader
>>>corpus_root = './'
>>>newcorpus = PlaintextCorpusReader(corpus_root, '.*')
>>>newcorpus.words()
如何newcorpus使用 punkt 分割句子？我尝试使用 punkt 函数，但 punkt 函数无法读取PlaintextCorpusReader类？
您还可以指导我如何将分段数据写入文本文件吗？
经过几年弄清楚它是如何工作的，这里是更新的教程
如何使用文本文件目录创建 NLTK 语料库？
主要思想是利用nltk.corpus.reader包。如果您有一个英文文本文件目录，最好使用PlaintextCorpusReader。
如果您有一个如下所示的目录：
newcorpus/
file1.txt
file2.txt
...
只需使用这些代码行，您就可以获得语料库：
import os
from nltk.corpus.reader.plaintext import PlaintextCorpusReader
corpusdir = 'newcorpus/' # Directory of corpus.
newcorpus = PlaintextCorpusReader(corpusdir, '.*')
注意：这PlaintextCorpusReader将使用默认值nltk.tokenize.sent_tokenize()并将nltk.tokenize.word_tokenize()您的文本拆分为句子和单词，这些功能是为英语构建的，它可能不适用于所有语言。
这是创建测试文本文件以及如何使用 NLTK 创建语料库以及如何在不同级别访问语料库的完整代码：
import os
from nltk.corpus.reader.plaintext import PlaintextCorpusReader
# Let's create a corpus with 2 texts in different textfile.
txt1 = """This is a foo bar sentence.\nAnd this is the first txtfile in the corpus."""
txt2 = """Are you a foo bar? Yes I am. Possibly, everyone is.\n"""
corpus = [txt1,txt2]
# Make new dir for the corpus.
corpusdir = 'newcorpus/'
if not os.path.isdir(corpusdir):
os.mkdir(corpusdir)
# Output the files into the directory.
filename = 0
for text in corpus:
filename+=1
with open(corpusdir+str(filename)+'.txt','w') as fout:
print>>fout, text
# Check that our corpus do exist and the files are correct.
assert os.path.isdir(corpusdir)
for infile, text in zip(sorted(os.listdir(corpusdir)),corpus):
assert open(corpusdir+infile,'r').read().strip() == text.strip()
# Create a new corpus by specifying the parameters
# (1) directory of the new corpus
# (2) the fileids of the corpus
# NOTE: in this case the fileids are simply the filenames.
newcorpus = PlaintextCorpusReader('newcorpus/', '.*')
# Access each file in the corpus.
for infile in sorted(newcorpus.fileids()):
print infile # The fileids of each file.
with newcorpus.open(infile) as fin: # Opens the file.
print fin.read().strip() # Prints the content of the file
print
# Access the plaintext; outputs pure string/basestring.
print newcorpus.raw().strip()
print
# Access paragraphs in the corpus. (list of list of list of strings)
# NOTE: NLTK automatically calls nltk.tokenize.sent_tokenize and
# nltk.tokenize.word_tokenize.
#
# Each element in the outermost list is a paragraph, and
# Each paragraph contains sentence(s), and
# Each sentence contains token(s)
print newcorpus.paras()
print
# To access pargraphs of a specific fileid.
print newcorpus.paras(newcorpus.fileids()[0])
# Access sentences in the corpus. (list of list of strings)
# NOTE: That the texts are flattened into sentences that contains tokens.
print newcorpus.sents()
print
# To access sentences of a specific fileid.
print newcorpus.sents(newcorpus.fileids()[0])
# Access just tokens/words in the corpus. (list of strings)
print newcorpus.words()
# To access tokens of a specific fileid.
print newcorpus.words(newcorpus.fileids()[0])
最后，要读取文本目录并创建其他语言的 NLTK 语料库，您必须首先确保您有一个 python 可调用的单词标记化和句子标记化模块，该模块接受字符串/基本字符串输入并产生这样的输出：
>>> from nltk.tokenize import sent_tokenize, word_tokenize
>>> txt1 = """This is a foo bar sentence.\nAnd this is the first txtfile in the corpus."""
>>> sent_tokenize(txt1)
['This is a foo bar sentence.', 'And this is the first txtfile in the corpus.']
>>> word_tokenize(sent_tokenize(txt1)[0])
['This', 'is', 'a', 'foo', 'bar', 'sentence', '.']
我认为PlaintextCorpusReader已经使用 punkt 分词器对输入进行了分段，至少如果您的输入语言是英语的话。
def __init__(self, root, fileids,
word_tokenizer=WordPunctTokenizer(),
sent_tokenizer=nltk.data.LazyLoader(
'tokenizers/punkt/english.pickle'),
para_block_reader=read_blankline_block,
encoding='utf8'):
您可以向读者传递一个单词和句子标记器，但对于后者，默认值已经是nltk.data.LazyLoader('tokenizers/punkt/english.pickle').
对于单个字符串，将按如下方式使用分词器（在此处解释，有关 punkt 分词器，请参阅第 5 节）。
>>> import nltk.data
>>> text = """
... Punkt knows that the periods in Mr. Smith and Johann S. Bach
... do not mark sentence boundaries. And sometimes sentences
... can start with non-capitalized words. i is a good variable
... name.
... """
>>> tokenizer = nltk.data.load('tokenizers/punkt/english.pickle')
>>> tokenizer.tokenize(text.strip())
>>> import nltk
>>> from nltk.corpus import PlaintextCorpusReader
>>> corpus_root = './'
>>> newcorpus = PlaintextCorpusReader(corpus_root, '.*')
"""
if the ./ dir contains the file my_corpus.txt, then you
can view say all the words it by doing this
"""
>>> newcorpus.words('my_corpus.txt')
from nltk.corpus.reader.plaintext import PlaintextCorpusReader
filecontent1 = "This is a cow"
filecontent2 = "This is a Dog"
corpusdir = 'nltk_data/'
with open(corpusdir + 'content1.txt', 'w') as text_file:
text_file.write(filecontent1)
with open(corpusdir + 'content2.txt', 'w') as text_file:
text_file.write(filecontent2)
text_corpus = PlaintextCorpusReader(corpusdir, ["content1.txt", "content2.txt"])
no_of_words_corpus1 = len(text_corpus.words("content1.txt"))
print(no_of_words_corpus1)
no_of_unique_words_corpus1 = len(set(text_corpus.words("content1.txt")))
no_of_words_corpus2 = len(text_corpus.words("content2.txt"))
no_of_unique_words_corpus2 = len(set(text_corpus.words("content2.txt")))
enter code here