Unigram means a single word. In a unigram tagger, a single token is used to find the particular parts-of-speech tag.
Training of UnigramTagger can be performed by providing it with a list of sentences at the time of initialization.
Let's see the following code in NLTK, which performs UnigramTagger training:
>>> import nltk
>>> from nltk.tag import UnigramTagger
>>> from nltk.corpus import treebank
>>> training= treebank.tagged_sents()[:7000]
>>> unitagger=UnigramTagger(training)
>>> treebank.sents()[0]
['Pierre', 'Vinken', ',', '61', 'years', 'old', ',', 'will', 'join', 'the', 'board', 'as', 'a', 'nonexecutive', 'director', 'Nov.', '29', '.']
>>> unitagger.tag(treebank.sents()[0])
[('Pierre', 'NNP'), ('Vinken', 'NNP'), (',', ','), ('61', 'CD'), ('years', 'NNS'), ('old', 'JJ'), (',', ','), ('will', 'MD'), ('join', 'VB'), ('the', 'DT'), ('board', 'NN'), ('as', 'IN'), ('a', 'DT'), ('nonexecutive', 'JJ'), ('director', 'NN'), ('Nov.', 'NNP'), ('29', 'CD'), ('.', '.')]In the preceding code, we have performed training using the first 7000 sentences of the Treebank corpus.
The hierarchy followed by UnigramTagger is depicted in the following inheritance diagram:
To evaluate UnigramTagger, let's see the following code, which calculates the accuracy:
>>> import nltk >>> from nltk.corpus import treebank >>> from nltk.tag import UnigramTagger >>> training= treebank.tagged_sents()[:7000] >>> unitagger=UnigramTagger(training) >>> testing = treebank.tagged_sents()[2000:] >>> unitagger.evaluate(testing) 0.963400866227395
So, it is 96% accurate in correctly performing pos tagging.
Since UnigramTagger inherits from ContextTagger, we can map the context key with a specific tag.
Consider the following example of tagging using UnigramTagger:
>>> import nltk
>>> from nltk.corpus import treebank
>>> from nltk.tag import UnigramTagger
>>> unitag = UnigramTagger(model={'Vinken': 'NN'})
>>> unitag.tag(treebank.sents()[0])
[('Pierre', None), ('Vinken', 'NN'), (',', None), ('61', None), ('years', None), ('old', None), (',', None), ('will', None), ('join', None), ('the', None), ('board', None), ('as', None), ('a', None), ('nonexecutive', None), ('director', None), ('Nov.', None), ('29', None), ('.', None)]Here, in the preceding code, UnigramTagger only tags 'Vinken' with the 'NN' tag and the rest are tagged with the 'None' tag since we have provided the tag for the word 'Vinken' in the context model and no other words are included in the context model.
In a given context, ContextTagger uses the frequency of a given tag to decide the occurrence of the most probable tag. In order to use minimum threshold frequency, we can pass a specific value to the cutoff value. Let's see the code that evaluates UnigramTagger:
>>> unitagger = UnigramTagger(training, cutoff=5) >>> unitagger.evaluate(testing) 0.7974218445306567
Backoff tagging may be defined as a feature of SequentialBackoffTagger. All the taggers are chained together so that if one of the taggers is unable to tag a token, then the token may be passed to the next tagger.
Let's see the following code, which uses back-off tagging. Here, DefaultTagger and UnigramTagger are used to tag a token. If any tagger of them is unable to tag a word, then the next tagger may be used to tag it:
>>> import nltk
>>> from nltk.tag import UnigramTagger
>>> from nltk.tag import DefaultTagger
>>> from nltk.corpus import treebank
>>> testing = treebank.tagged_sents()[2000:]
>>> training= treebank.tagged_sents()[:7000]
>>> tag1=DefaultTagger('NN')
>>> tag2=UnigramTagger(training,backoff=tag1)
>>> tag2.evaluate(testing)
0.963400866227395The subclasses of NgramTagger areUnigramTagger, BigramTagger, and TrigramTagger. BigramTagger makes use of the previous tag as contextual information. TrigramTagger uses the previous two tags as contextual information.
Consider the following code, which illustrates the implementation of BigramTagger:
>>> import nltk
>>> from nltk.tag import BigramTagger
>>> from nltk.corpus import treebank
>>> training_1= treebank.tagged_sents()[:7000]
>>> bigramtagger=BigramTagger(training_1)
>>> treebank.sents()[0]
['Pierre', 'Vinken', ',', '61', 'years', 'old', ',', 'will', 'join', 'the', 'board', 'as', 'a', 'nonexecutive', 'director', 'Nov.', '29', '.']
>>> bigramtagger.tag(treebank.sents()[0])
[('Pierre', 'NNP'), ('Vinken', 'NNP'), (',', ','), ('61', 'CD'), ('years', 'NNS'), ('old', 'JJ'), (',', ','), ('will', 'MD'), ('join', 'VB'), ('the', 'DT'), ('board', 'NN'), ('as', 'IN'), ('a', 'DT'), ('nonexecutive', 'JJ'), ('director', 'NN'), ('Nov.', 'NNP'), ('29', 'CD'), ('.', '.')]
>>> testing_1 = treebank.tagged_sents()[2000:]
>>> bigramtagger.evaluate(testing_1)
0.922942709936983Let's see another code for BigramTagger and TrigramTagger:
>>> import nltk >>> from nltk.tag import BigramTagger, TrigramTagger >>> from nltk.corpus import treebank >>> testing = treebank.tagged_sents()[2000:] >>> training= treebank.tagged_sents()[:7000] >>> bigramtag = BigramTagger(training) >>> bigramtag.evaluate(testing) 0.9190426339881356 >>> trigramtag = TrigramTagger(training) >>> trigramtag.evaluate(testing) 0.9101956195989079
NgramTagger can be used to generate a tagger for n greater than three as well. Let's see the following code in NLTK, which develops QuadgramTagger:
>>> import nltk >>> from nltk.corpus import treebank >>> from nltk import NgramTagger >>> testing = treebank.tagged_sents()[2000:] >>> training= treebank.tagged_sents()[:7000] >>> quadgramtag = NgramTagger(4, training) >>> quadgramtag.evaluate(testing) 0.9429767842847466
The AffixTagger is also a ContextTagger in that makes use of a prefix or suffix as the contextual information.
Let's see the following code, which uses AffixTagger:
>>> import nltk >>> from nltk.corpus import treebank >>> from nltk.tag import AffixTagger >>> testing = treebank.tagged_sents()[2000:] >>> training= treebank.tagged_sents()[:7000] >>> affixtag = AffixTagger(training) >>> affixtag.evaluate(testing) 0.29043249789601167
Let's see the following code, which learns the use of four character prefixes:
>>> import nltk >>> from nltk.tag import AffixTagger >>> from nltk.corpus import treebank >>> testing = treebank.tagged_sents()[2000:] >>> training= treebank.tagged_sents()[:7000] >>> prefixtag = AffixTagger(training, affix_length=4) >>> prefixtag.evaluate(testing) 0.21103516226368618
Consider the following code, which learns the use of three character suffixes:
>>> import nltk >>> from nltk.tag import AffixTagger >>> from nltk.corpus import treebank >>> testing = treebank.tagged_sents()[2000:] >>> training= treebank.tagged_sents()[:7000] >>> suffixtag = AffixTagger(training, affix_length=-3) >>> suffixtag.evaluate(testing) 0.29043249789601167
Consider the following code in NLTK, which that combines many affix taggers in the back-off chain:
>>> import nltk >>> from nltk.tag import AffixTagger >>> from nltk.corpus import treebank >>> testing = treebank.tagged_sents()[2000:] >>> training= treebank.tagged_sents()[:7000] >>> prefixtagger=AffixTagger(training,affix_length=4) >>> prefixtagger.evaluate(testing) 0.21103516226368618 >>> prefixtagger3=AffixTagger(training,affix_length=3,backoff=prefixtagger) >>> prefixtagger3.evaluate(testing) 0.25906767658107027 >>> suffixtagger3=AffixTagger(training,affix_length=-3,backoff=prefixtagger3) >>> suffixtagger3.evaluate(testing) 0.2939630929654946 >>> suffixtagger4=AffixTagger(training,affix_length=-4,backoff=suffixtagger3) >>> suffixtagger4.evaluate(testing) 0.3316090892296324
The TnT is Trigrams n Tags. TnT is a statistical-based tagger that is based on the second order Markov models.
Let's see the code in NLTK for TnT:
>>> import nltk >>> from nltk.tag import tnt >>> from nltk.corpus import treebank >>> testing = treebank.tagged_sents()[2000:] >>> training= treebank.tagged_sents()[:7000] >>> tnt_tagger=tnt.TnT() >>> tnt_tagger.train(training) >>> tnt_tagger.evaluate(testing) 0.9882176652913768
TnT computes ConditionalFreqDist and internalFreqDist from the training text. These instances are used to compute unigrams, bigrams, and trigrams. In order to choose the best tag, TnT uses the ngram model.
Consider the following code of a DefaultTagger in which, if the value of the unknown tagger is provided explicitly, then TRAINED will be set to TRUE:
>>> import nltk
>>> from nltk.tag import DefaultTagger
>>> from nltk.tag import tnt
>>> from nltk.corpus import treebank
>>> testing = treebank.tagged_sents()[2000:]
>>> training= treebank.tagged_sents()[:7000]
>>> tnt_tagger=tnt.TnT()
>>> unknown=DefaultTagger('NN')
>>> tagger_tnt=tnt.TnT(unk=unknown,Trained=True)
>>> tnt_tagger.train(training)
>>> tnt_tagger.evaluate(testing)
0.988238192006897