NLP | Backoff Tagging for combining taggers

What is part-of-speech (POS) tagging?
This is the process of converting sentences to forms — a list of words, a list of tuples (where each tuple is of the form (word, tag)). The tag in the case of a word is a part of speech tag and indicates whether the word is a noun, adjective, verb, etc.

What is Backoff Tagging?
This is one of the most important functions of SequentialBackoffTagger, as it allows taggers to be combined together. The advantage of this is that if the tagger is unaware of the tagging of a word, it can pass this tagging task to the next return tag. If he cannot do this, he can pass the word
the next return tag, and so on until it remains to check the return tags.

Code # 1: Executing tags

# Loading libraries

from nltk.tag import SequentialBackoffTagger

from nltk.tag import DefaultTagger 

from nltk.tag import UnigramTagger 


from nltk.corpus import treebank

# initialize learning and testing the suite

train_data = treebank.tagged_sents () [: 3000 ]

test_data = treebank.tagged_sents () [ 3000 :]

# Tag definition

tag1 = DefaultTagger ( `NN` )

# Tag

tag2 = Unig ramTagger (train_data, backoff = tag1)

# Rating
tag2.evaluate (test_data)



How does it work?
The SequentialBackoffTagger class can take a backoff keyword argument, whose value is another instance of SequentialBackoffTagger. In the above code, the unigram part-of-speech tagger is rolled back with the Default tag and trained on the treebank.tagged_sents () dataset .

Code # 2 : Preparing an internal list of rollback tags

from nltk.tag import SequentialBackoffTagger


print (tag1._taggers = = [tag1])


print ( "" , tag2. _taggers = = [tag2, tag1])


 True True 

How does it work?

  • The SequentialBackoffTagger class is initialized, creating an internal list of backoff tags with the first element being itself.
  • An internal list of backoff tags is added if the backoff tag is specified.
  • The SequentialBackoffTagger class uses _taggers list — an internal list of rollback tags when the tag () method is called.
  • By calling choose_tag () on each one, it loops through its own tag list.
  • Stops and returns a tag when a tag is found.
  • The tag will be returned if the main tagger can tag the word.
  • Otherwise, it returns None and tries the next tag, and so on, until the tag is found, or None is returned .

Code # 3: Save and load trained brine tagger.

# Loading Libraries

import pickle

# Open file and write

file = open ( ` tagger.pickle` , `wb` )

pickle.dump (tagger, file )

file . close ()

# Reading the file

file = open ( `tagger.pickle ` , ` rb` )

# Loading

tagger = pickle.load (f)

Output: (`tagger.pickle`) will load the file