Python NLTK Chunking with NLTK














































Python NLTK Chunking with NLTK



CHUNKING WITH NLTK

Chunking is used to add more structure to the sentence by following parts of speech (POS) tagging.

It is also known as shallow parsing.

The resulted group of words is called "chunks."

In shallow parsing, there is a maximum one level between roots and leaves while deep parsing comprises more than one level. Shallow Parsing is also called light parsing or chunking.

Rules for Chunking:

There are no pre-defined rules, but you can combine them
according to needs and requirements.

chunk:{<NN.?>*<VBD.?>*<JJ.?>*<CC>?}

Following table shows what the various symbol
means:


Name of symbol


Description


.


Any character except newline


*


Match 0 or more repetitions


?


Match 0 or 1 repetition

Uses of chunking:

Chunking is used for
entity detection. An entity is that part of the sentence by which the machine gets
the value for any intention
.

In other words, chunking
is used as selecting the subsets of tokens.

The primary usage of
chunking is to make a group of "noun phrases." The parts of speech
are combined with regular expressions.

Let us see program understand rules better:


Output:                              


Program to demonstrate the use case of chunking:   


Output:                  


Graph:            


Conclusion:    

Chunking is used to categorize different tokens into the same chunk. The result will depend on grammar which has been selected


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