使用 NLTK 创建新语料库

Question

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 为语料库 nltk_data 提供的语料库函数。

我尝试过 PlaintextCorpusReader 但我无法进一步了解：

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

How do I split the newcorpus Sentence using punkt?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？

Answer 1

经过几年的了解它是如何工作的，这里有

如何使用文本文件目录创建 NLTK 语料库？

的更新教程，主要思想是利用 nltk.corpus.reader 包。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
         ...

只需使用这些代码行，您就可以获得一个语料库：

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() 将文本拆分为句子和单词，这些函数是为英语构建的，可能不 适用于所有语言。

以下是创建测试文本文件、如何使用 NLTK 创建语料库以及如何访问不同级别的语料库

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])

的完整代码：最后，要读取文本目录并以其他语言创建 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，它接受字符串/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', '.']

Answer 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 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'):

可以向读者传递单词和句子标记器，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，分词器将按如下方式使用（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())

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')

Answer 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