我认为我的标题的答案通常是去阅读文档，但我浏览了 NLTK book但它没有给出答案。我是 Python 的新手。

我有一堆.txt文件，我希望能够使用NLTK为语料库nltk_data提供的语料库函数。

我已经尝试过 PlaintextCorpusReader 但我无法获得更多:

>>>import nltk
>>>from nltk.corpus import PlaintextCorpusReader
>>>corpus_root = './'
>>>newcorpus = PlaintextCorpusReader(corpus_root, '.*')
>>>newcorpus.words()

如何使用 punkt 分割 newcorpus 句子？我尝试使用 punkt 函数，但 punkt 函数无法读取 PlaintextCorpusReader 类？

你能不能也告诉我如何将分段数据写入文本文件？

最佳答案

经过几年弄清楚它是如何工作的，这里是

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包裹。如果您有一个英语 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
         ...

只需使用这几行代码，你就可以得到一个语料库:

import os
from nltk.corpus.reader.plaintext import PlaintextCorpusReader

corpusdir = 'newcorpus/' # Directory of corpus.

newcorpus = PlaintextCorpusReader(corpusdir, '.*')

注意: PlaintextCorpusReader 将使用默认的 nltk.tokenize.sent_tokenize() 和 nltk.tokenize.word_tokenize( ) 将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。

下面是创建测试文本文件的完整代码，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:

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，要阅读文本目录并创建其他语言的 NLTK 语料库，您必须首先确保您拥有 Python 可调用的单词标记化和句子标记化接受字符串/基本字符串输入并产生此类输出的模块:

>>> 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', '.']

关于python - 使用 NLTK 创建新语料库，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/4282227/