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:该PlaintextCorpusReader
将使用默认nltk.tokenize.sent_tokenize()
和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 单词标记化和句子标记化模块,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', '.']
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: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?