我认为我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
提供的语料库函数。
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?
最佳答案
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包裹。如果您有 English 的文本文件目录,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,它可能不适用于所有语言。
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', '.']
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/4951751/