问题描述
我正在寻找一种在过去，现在和将来时态中给英语文本计数动词短语的方法.现在，我正在使用 NLTK ，进行POS(词性)标记，然后算上'VBD以获取过去时态.不过，这还不够准确，因此我想我需要走得更远并使用分块，然后分析VP分块以获取特定的时态模式.有什么可以做的吗?任何进一步的阅读可能会有所帮助? NLTK书主要针对NP块，而我几乎找不到有关VP块的信息.
I am looking for a way given an English text count verb phrases in it in past, present and future tenses. For now I am using NLTK, do a POS (Part-Of-Speech) tagging, and then count say 'VBD' to get past tenses. This is not accurate enough though, so I guess I need to go further and use chunking, then analyze VP-chunks for specific tense patterns. Is there anything existing that does that? Any further reading that might be helpful? The NLTK book is focused mostly on NP-chunks, and I can find quite few info on VP-chunks.
推荐答案
确切的答案取决于您打算使用哪个分块器，但是列表理解将使您走很长一段路.使用不存在的分块器，可以获取动词短语的数量.
Thee exact answer depends on which chunker you intend to use, but list comprehensions will take you a long way. This gets you the number of verb phrases using a non-existent chunker.
len([phrase for phrase in nltk.Chunker(sentence) if phrase[1] == 'VP'])
您可以采用更细粒度的方法来检测时态数.
You can take a more fine-grained approach to detect numbers of tenses.
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