如何使用NLTK在python中获取句子列表的通用标签模式 [英] How to get common tag pattern for sentences list in python with NLTK

问题描述

这里有一个句子列表.使用NLTK，我可以标记句子并获得该句子的标记模式.这样就可以得到整个列表的标签模式，但是我想要的是找出大多数句子都可以匹配的常见标签模式，例如:

Here I have a list of sentences.With NLTK I can tag the sentence and get the tag pattern of that sentences. So as like this I can get the tag patterns for the whole list.But what I wanted is to identify the common tag patterns which most sentences get matched.For example:

• 什么是封装
  

• What is encapsulation
  

tag pattern : {<WP><VBZ><NN>}

你的婚礼怎么样

How was your wedding

tag pattern : {<WRB><VBD><PRP$><NN>}

您今天的计划是什么

What is your plan today

tag pattern : {<WP><VBZ><PRP$><NN><NN>}

因此上述三句话的常用标记模式(结合正则表达式标记器)是:

So the common tag pattern(Combining regexp tagger) for above threes sentences is:

{<W.+><V.+><PRP.?>?<NN>+} - One "Wh" word,one verb,zero or one pronoun,one or many nouns

所以我想将句子的标记模式概括为普通的标记模式.这就是我想做的事情.

So I want to generalize the tag patterns of sentences to common ones.This is the thing what I wanted to do..

那么有人可以告诉我该怎么做吗?

So can someone tell me how to do that?

推荐答案

听起来您正在使用正则表达式(带有量词)，该正则表达式将匹配数据中的所有不同标记序列.虽然这不是一个简单的问题， 我怀疑您的目标是找到一种模式来捕获合法句子的序列，对吗?

It sounds like you are after a regexp (with quantifiers) that will match all the different tag sequences in your data. While this is not an easy problem, I suspect that your goal is to find a pattern that captures the sequences that are legal sentences, is this right?

如果是这样，则正则表达式(通常是有限状态方法)天生就是用于此工作的错误工具.为了甚至开始刻画句子集合的特征，您需要查看上下文无关的语法.看一下有关该主题的NLTK资料.

If so, regexps (and finite-state approaches in general) are inherently the wrong tool for the job. To even get a start on characterizing your sentence collection, you need to look at context-free grammars. Take a look at the NLTK's materials on the topic.

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