使用NLTK创建新的语料库
nlp
nltk
python
6
0

我认为标题的答案通常是去阅读文档,但是我遍历了NLTK书,但没有给出答案。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nltk_data

我已经尝试了PlaintextCorpusReader但是我PlaintextCorpusReader什么:

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

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

您还可以引导我介绍如何将分段数据写入文本文件吗?

参考资料:
Stack Overflow
收藏
评论
共 3 个回答
高赞 时间 活跃

经过数年弄清楚其工作原理后,这是

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。如果您有英文文本文件目录,则最好使用PlaintextCorpusReader

如果您的目录如下所示:

newcorpus/
         file1.txt
         file2.txt
         ...

只需使用以下代码行,您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:PlaintextCorpusReader将使用默认nltk.tokenize.sent_tokenize()nltk.tokenize.word_tokenize()到你的文本拆分成句子和单词与这些功能构建英语,可能所有的语言无法正常工作。

这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一个python-callable 单词标记化句子标记化模块,这些模块接受字符串/基字符串输入并产生以下输出:

>>> 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', '.']
收藏
评论
 >>> 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')
收藏
评论

我认为PlaintextCorpusReader已经用punkt标记器对输入进行了细分,至少在您的输入语言是英语的情况下。

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'):

您可以向读者传递一个单词和句子标记器,但是对于后者,默认值已经是nltk.data.LazyLoader('tokenizers/punkt/english.pickle')

对于单个字符串,将按以下方式使用标记符( 此处说明,有关punkt标记符,请参见第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())
收藏
评论
新手导航
  • 社区规范
  • 提出问题
  • 进行投票
  • 个人资料
  • 优化问题
  • 回答问题

关于我们

常见问题

内容许可

联系我们

@2020 AskGo
京ICP备20001863号