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.
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, affix_length=-2) >>> suffix_tagger.evaluate(test_sents) 0.31940427368875457
A positive value for affix_length means that the AffixTagger class will learn word prefixes, essentially word[:affix_length]. If affix_length is negative, then suffixes are learned using word[affix_length:].
You can combine multiple affix taggers in a backoff chain if you want to learn on multiple character length affixes. Here's an example of four AffixTagger classes learning on 2 and 3 character prefixes and suffixes:
>>> pre3_tagger = AffixTagger(train_sents, affix_length=3) >>> pre3_tagger.evaluate(test_sents) 0.23587308439456076 >>> pre2_tagger = AffixTagger(train_sents, affix_length=2, backoff=pre3_tagger) >>> pre2_tagger.evaluate(test_sents) 0.29786315562270665 >>> suf2_tagger = AffixTagger(train_sents, affix_length=-2, backoff=pre2_tagger) >>> suf2_tagger.evaluate(test_sents) 0.32467083962875026 >>> suf3_tagger = AffixTagger(train_sents, affix_length=-3, backoff=suf2_tagger) >>> suf3_tagger.evaluate(test_sents) 0.3590761925318368
As you can see, the accuracy goes up each time.
The AffixTagger class also takes a min_stem_length keyword argument, with a default value of 2. If the word length is less than min_stem_length plus the absolute value of affix_length, then None is returned by the context() method. Increasing min_stem_length forces the AffixTagger class to only learn on longer words, while decreasing min_stem_length will allow it to learn on shorter words. Of course, for shorter words, the affix_length argument could be equal to or greater than the word length, and AffixTagger would essentially be acting like a UnigramTagger class.