使用 NLTK 创建新语料库 [英] Creating a new corpus with NLTK

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.

我已经尝试过 PlaintextCorpusReader 但我无法进一步:

>>>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?

您能否也指导我如何将分段数据写入文本文件?

解决方案

我认为 PlaintextCorpusReader 已经使用 punkt 分词器对输入进行了分割，至少如果您的输入语言是英语.

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，编码='utf8'):

您可以向读者传递一个词和句子标记器，但后者的默认值已经是 nltk.data.LazyLoader('tokenizers/punkt/english.pickle').

对于单个字符串，将按如下方式使用分词器(在 here，请参阅第 5 节了解 punkt 标记器).

<预><代码>>>>导入 nltk.data>>>文字 = """... Punkt 知道 Smith 先生和 Johann S. Bach 中的时期...不要标记句子边界.有时还有句子... 可以以非大写的单词开头.我是一个很好的变量... 名称.……">>>tokenizer = nltk.data.load('tokenizers/punkt/english.pickle')>>>tokenizer.tokenize(text.strip())

I reckoned that often the answer to my title is to go and read the documentations, but I ran through the NLTK book but it doesn't give the answer. I'm kind of new to Python.

I have a bunch of .txt files and I want to be able to use the corpus functions that NLTK provides for the corpus nltk_data.

I've tried PlaintextCorpusReader but I couldn't get further than:

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

How do I segment the newcorpus sentences using punkt? I tried using the punkt functions but the punkt functions couldn't read PlaintextCorpusReader class?

Can you also lead me to how I can write the segmented data into text files?

解决方案

I think the PlaintextCorpusReader already segments the input with a punkt tokenizer, at least if your input language is english.

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'):

You can pass the reader a word and sentence tokenizer, but for the latter the default already is nltk.data.LazyLoader('tokenizers/punkt/english.pickle').

For a single string, a tokenizer would be used as follows (explained here, see section 5 for punkt tokenizer).

>>> 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())

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