A unigram generally refers to a single token. Therefore, a unigram tagger only uses a single word as its context for determining the part-of-speech tag.
UnigramTagger inherits from NgramTagger, which is a subclass of ContextTagger, which inherits from SequentialBackoffTagger. In other words, UnigramTagger is a context-based tagger whose context is a single word, or unigram.
UnigramTagger can be trained by giving it a list of tagged sentences at initialization.
>>> from nltk.tag import UnigramTagger
>>> from nltk.corpus import treebank
>>> train_sents = treebank.tagged_sents()[:3000]
>>> tagger = UnigramTagger(train_sents)
>>> treebank.sents()[0]
['Pierre', 'Vinken', ',', '61', 'years', 'old', ',', 'will', 'join', 'the', 'board', 'as', 'a', 'nonexecutive', 'director', 'Nov.', '29', '.']
>>> tagger.tag(treebank.sents()[0])
[('Pierre', 'NNP'), ('Vinken', 'NNP'), (',', ','), ('61', 'CD...
