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 Librariesfrom tag_util import patternsfrom nltk.tag import RegexpTaggerfrom nltk.corpus import treebanktest_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 librariesfrom tag_util import word_tag_modelfrom nltk.corpus import treebankfrom 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 taggertag = AffixTagger(train_data)# Testingprint ("\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 prefixesprefix_tag = AffixTagger(train_data, affix_length = 3)# Testingaccuracy = prefix_tag.evaluate(test_data)print ("Accuracy : ", accuracy) |
Output :
Accuracy : 0.23587308439456076
Code #5 : AffixTagger by specifying 2-character suffixes
Python3
# Specifying 2 character suffixessufix_tag = AffixTagger(train_data, affix_length = -2)# Testingaccuracy = sufix_tag.evaluate(test_data)print ("Accuracy : ", accuracy) |
Output :
Accuracy : 0.31940427368875457
