使用 NLTK 创建新语料库

Posted 2023-02-15

【中文标题】使用 NLTK 创建新语料库

【英文标题】：Creating a new corpus with NLTK

【发布时间】：2011-06-24 12:25:38

【问题描述】：

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。我对 Python 有点陌生。

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

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？

您能否指导我如何将分段数据写入文本文件？

【参考方案1】：

经过几年的摸索，这里是

的更新教程

如何创建带有文本文件目录的 NLTK 语料库？

主要思想是利用nltk.corpus.reader 包。如果您有一个English 文本文件目录，最好使用PlaintextCorpusReader。

如果您的目录如下所示：

newcorpus/
         file1.txt
         file2.txt
         ...

只需使用这几行代码，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() 将您的文本分成句子和单词，这些功能是为英语构建的，它可能 NOT 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，您必须首先确保您具有可 Python 调用的 word tokenization 和 sentence tokenization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', '.']

【讨论】：

感谢您的澄清。不过，默认情况下支持许多语言。

如果有人收到AttributeError: __exit__ 错误。使用open() 而不是with()

【参考方案2】：

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。

PlainTextCorpusReader's constructor

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'):

您可以将单词和句子标记器传递给读者，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，分词器将按如下方式使用（解释为 here，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())

【讨论】：

感谢您的解释。知道了。但是如何将分段的句子输出到单独的 txt 文件中？

"The NLTK data package includes a pre-trained Punkt tokenizer for English."

【参考方案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')

【讨论】：

为 devnagari 语言解决一些问题。

【参考方案4】：

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