使用NLTK创建新的语料库

Question

我估计通常我的标题的答案是去阅读文档,但我浏览了NLTK书,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为语料库提供的语料库功能nltk_data.

我已经尝试PlaintextCorpusReader但是我无法进一步:

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

如何newcorpus使用punkt 对句子进行分段？我尝试使用punkt函数,但punkt函数无法读取PlaintextCorpusReader类？

你能否告诉我如何将分段数据写入文本文件？

编辑: 这个问题有一次赏金,它现在有第二个赏金.请参阅赏金框中的文字.

Answer 1

经过几年的研究,它是如何工作的,这里是更新的教程

如何使用textfiles目录创建NLTK语料库？

主要思想是使用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:该PlaintextCorpusReader会使用默认的nltk.tokenize.sent_tokenize()和nltk.tokenize.word_tokenize()你的文章分成句子和单词和这些功能都建立英语中,它可能不是所有的语言.

以下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,要读取文本目录并使用其他语言创建NLTK语料库,首先必须确保您具有python可调用的单词标记化和句子标记化模块,它们接受字符串/基本字符串输入并生成此类输出:

>>> 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', '.']

Answer 2

我认为PlaintextCorpusReader已经使用punkt tokenizer对输入进行了分段,至少如果您的输入语言是英语.

PlainTextCorpusReader的构造函数

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'):

你可以向读者传递一个单词和一个句子标记器,但对于后者,默认已经是nltk.data.LazyLoader('tokenizers/punkt/english.pickle').

对于单个字符串,将使用如下的标记化器(在此处解释,请参阅第5节中的punkt标记生成器).

>>> 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())

Answer 3

 >>> 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')