Skip to content
Related Articles

Related Articles

NLP | Regex and Affix tagging

View Discussion
Improve Article
Save Article
  • Last Updated : 24 Sep, 2021
View Discussion
Improve Article
Save Article

Regular expression matching is used to tag words. Consider the example, numbers can be matched with \d to assign the tag CD (which refers to a Cardinal number). Or one can match the known word patterns, such as the suffix “ing”. 

Understanding the concept – 

  • RegexpTagger is a subclass of SequentialBackoffTagger. It can be positioned before a DefaultTagger class so as to tag words that the n-gram tagger(s) missed and thus can be a useful part of a backoff chain.
  • At initialization, patterns are saved in RegexpTagger class. choose_tag() is then called, it iterates over the patterns. Then, it returns the first expression tag that can match the current word using re.match().
  • So, if the two given expressions get matched, then the tag of the first one will be returned without even trying the second expression.
  • If the given pattern is like – (r’.*’, ‘NN’), RegexpTagger class can replace the DefaultTagger class

Code #1 : Python regular expression module and re syntax 

Python3




patterns = [(r'^\d+$', 'CD'),
            # gerunds, i.e. wondering
            (r'.*ing$', 'VBG'),
            # i.e. wonderment
            (r'.*ment$', 'NN'),
            # i.e. wonderful
            (r'.*ful$', 'JJ')]

RegexpTagger class expects a list of two tuples 

-> first element in the tuple is a regular expression
-> second element is the tag

Code #2 : Using RegexpTagger  

Python3




# Loading Libraries
from tag_util import patterns
from nltk.tag import RegexpTagger
from nltk.corpus import treebank
 
test_data = treebank.tagged_sents()[3000:]
 
tagger = RegexpTagger(patterns)
print ("Accuracy : ", tagger.evaluate(test_data))

Output : 

Accuracy : 0.037470321605870924

What is Affix tagging? 
It is a subclass of ContextTagger. In the case of AffixTagger class, the context is either the suffix or the prefix of a word. So, it clearly indicates that this class can learn tags based on fixed-length substrings of the beginning or end of a word. 
It specifies the three-character suffixes. That words must be at least 5 characters long and None is returned as the tag if a word is less than five character.

Code #3 : Understanding AffixTagger. 

Python3




# loading libraries
from tag_util import word_tag_model
from nltk.corpus import treebank
from nltk.tag import AffixTagger
 
# initializing training and testing set   
train_data = treebank.tagged_sents()[:3000]
test_data = treebank.tagged_sents()[3000:]
 
print ("Train data : \n", train_data[1])
 
# Initializing tagger
tag = AffixTagger(train_data)
 
# Testing
print ("\nAccuracy : ", tag.evaluate(test_data))

Output : 

Train data :  
[('Mr.', 'NNP'), ('Vinken', 'NNP'), ('is', 'VBZ'), ('chairman', 'NN'), 
('of', 'IN'), ('Elsevier', 'NNP'), ('N.V.', 'NNP'), (', ', ', '), ('the', 'DT'),
('Dutch', 'NNP'), ('publishing', 'VBG'), ('group', 'NN'), ('.', '.')]

Accuracy : 0.27558817181092166

Code #4 : AffixTagger by specifying 3 character prefixes.  

Python3




# Specifying 3 character prefixes
prefix_tag = AffixTagger(train_data,
                         affix_length = 3)
 
# Testing
accuracy = prefix_tag.evaluate(test_data)
 
print ("Accuracy : ", accuracy)

Output : 

Accuracy : 0.23587308439456076

Code #5 : AffixTagger by specifying 2-character suffixes  

Python3




# Specifying 2 character suffixes
sufix_tag = AffixTagger(train_data,
                         affix_length = -2)
 
# Testing
accuracy = sufix_tag.evaluate(test_data)
 
print ("Accuracy : ", accuracy)

Output : 

Accuracy : 0.31940427368875457

 


My Personal Notes arrow_drop_up
Recommended Articles
Page :

Start Your Coding Journey Now!