Affix tagging

The AffixTagger class is another ContextTagger subclass, but this time the context is either the prefix or the suffix of a word. This means the AffixTagger class is able to learn tags based on fixed-length substrings of the beginning or ending of a word.

How to do it...

The default arguments for an AffixTagger class specify three-character suffixes, and that words must be at least five characters long. If a word is less than five characters, then None is returned as the tag.

>>> from nltk.tag import AffixTagger
>>> tagger = AffixTagger(train_sents)
>>> tagger.evaluate(test_sents)
0.27558817181092166

So, it does ok by itself with the default arguments. Let's try it by specifying three-character prefixes.

>>> prefix_tagger = AffixTagger(train_sents, affix_length=3)
>>> prefix_tagger.evaluate(test_sents)
0.23587308439456076

To learn on two-character suffixes, the code will look like this:

>>> suffix_tagger = AffixTagger(train_sents...