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NLP | Customization Using Tagged Corpus Reader

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How we can use Tagged Corpus Reader ? 
 

  • Customizing word tokenizer
  • Customizing sentence tokenizer
  • Customizing paragraph block reader
  • Customizing tag separator
  • Converting tags to a universal tagset


 


Code #1 : Customizing word tokenizer 
 

Python3

# Loading the libraries
from nltk.tokenize import SpaceTokenizer
from nltk.corpus.reader import TaggedCorpusReader
 
x = TaggedCorpusReader('.', r'.*\.pos',
                       word_tokenizer = SpaceTokenizer())
 
x.words()

                    

Output : 
 

['The', 'expense', 'and', 'time', 'involved', 'are', ...]


Code #2 : For sentence 
 

Python3

# Loading the libraries
from nltk.tokenize import LineTokenizer
from nltk.corpus.reader import TaggedCorpusReader
 
x = TaggedCorpusReader('.', r'.*\.pos',
                       sent_tokenizer = LineTokenizer())
 
x.sents()

                    

Output : 
 

[['The', 'expense', 'and', 'time', 'involved', 'are', 'astronomical', '.']]


Customizing paragraph 
 

  • Assume paragraphs to be split by blank lines
  • Done with the para_block_reader function, which is nltk.corpus.reader.util.read_blankline_block
  • Number of other block reader are present in nltk.corpus.reader.util, whose purpose is to read blocks of text from a stream.


Customizing Tag separator 
 

  • If ‘/’ is not used as the word/tag separator, one can pass an alternative string to TaggedCorpusReader for sep.
  • Default is sep=’/’, but if one wants to split words and tags with ‘|’, such as ‘word|tag’, then sep=’|’ is passed in .


Converting tags to a universal tagset 
Tagset : It is a list of POS tags used by one or more corpora. 
Universal Tagset : It is a simplified and condensed tagset composed of only 12 part-of-speech tags
Code #3 : map corpus tags to the universal tagset 
 

Python3

from nltk.corpus.reader import TaggedCorpusReader
 
x = TaggedCorpusReader('.', r'.*\.pos', tagset ='en-brown')
x.tagged_words(tagset ='universal')

                    

Output : 
 

[('The', 'DET'), ('expense', 'NOUN'), ('and', 'CONJ'), ...] 


Code #4 : map corpus tags to the universal tagset 
 

Python3

from nltk.corpus.reader import TaggedCorpusReader
from nltk.corpus import treebank
 
treebank.tagged_words()
 
treebank.tagged_words(tagset ='universal')
 
treebank.tagged_words(tagset ='brown')

                    

Output : 
 

[('Pierre', 'NNP'), ('Vinken', 'NNP'), (', ', ', '), ...]

[('Pierre', 'NOUN'), ('Vinken', 'NOUN'), (', ', '.'), …]

[('Pierre', 'UNK'), ('Vinken', 'UNK'), (', ', 'UNK'), ...]


 



Last Updated : 17 Jun, 2021
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