NLP | Combining NGram Taggers
NgramTagger has 3 subclasses
- UnigramTagger
- BigramTagger
- TrigramTagger
BigramTagger subclass uses previous tag as part of its context
TrigramTagger subclass uses the previous two tags as part of its context.
ngram – It is a subsequence of n items.
Idea of NgramTagger subclasses :
- By looking at the previous words and P-O-S tags, part-of-speech tag for the current word can be guessed.
- Each tagger maintains a context dictionary (ContextTagger parent class is used to implement it).
- This dictionary is used to guess that tag based on the context.
- The context is some number of previous tagged words in the case of NgramTagger subclasses.
Code #1 : Working of Bigram tagger
# Loading Libraries from nltk.tag import DefaultTagger from nltk.tag import BigramTagger from nltk.corpus import treebank # initializing training and testing set train_data = treebank.tagged_sents()[: 3000 ] test_data = treebank.tagged_sents()[ 3000 :] # Tagging tag1 = BigramTagger(train_data) # Evaluation tag1.evaluate(test_data) |
Output :
0.11318799913662854
Code #2 : Working of Trigram tagger
# Loading Libraries from nltk.tag import DefaultTagger from nltk.tag import TrigramTagger from nltk.corpus import treebank # initializing training and testing set train_data = treebank.tagged_sents()[: 3000 ] test_data = treebank.tagged_sents()[ 3000 :] # Tagging tag1 = TrigramTagger(train_data) # Evaluation tag1.evaluate(test_data) |
Output :
0.06876753723289446
Code #3 : Collectively using Unigram, Bigram and Trigram tagger.
# Loading Libraries from nltk.tag import TrigramTagger from tag_util import backoff_tagger from nltk.corpus import treebank # initializing training and testing set train_data = treebank.tagged_sents()[: 3000 ] test_data = treebank.tagged_sents()[ 3000 :] backoff = DefaultTagger( 'NN' ) tag = backoff_tagger(train_sents, [UnigramTagger, BigramTagger, TrigramTagger], backoff = backoff) tag.evaluate(test_sents) |
Output :
0.8806820634578028
How it works ?
- The backoff_tagger function creates an instance of each tagger class.
- It gives previous tagger and train_sents as a backoff.
- The order of tagger classes is important: In the code above the first class is UnigramTagger and hence, it will be trained first and given the initial backoff tagger (the DefaultTagger).
- This tagger then becomes the backoff tagger for the next tagger class.
- Final tagger returned will be an instance of the last tagger class – TrigramTagger.
Code #4 : Proof
print (tagger._taggers[ - 1 ] = = backoff) print ( "\n" , isinstance (tagger._taggers[ 0 ], TrigramTagger)) print ( "\n" , isinstance (tagger._taggers[ 1 ], BigramTagger)) |
Output :
True True True