NLTK trainer makes it easy to train part-of-speech taggers with various algorithms using train_tagger.py.

Training Sequential Backoff Taggers

The fastest algorithms are the sequential backoff taggers. You can specify the backoff sequence using the <span class="pre">--sequential</span> argument, which accepts any combination of the following letters:

a:	AffixTagger
u:	UnigramTagger
b:	BigramTagger
t:	TrigramTagger

For example, to train the same kinds of taggers that were used in Part of Speech Tagging with NLTK Part 1 – Ngram Taggers, you could do the following:

python train_tagger.py treebank --sequential ubt

You can rearrange ubt any way you want to change the order of the taggers (though ubt is generally the most accurate order).

Training Affix Taggers

The <span class="pre">--sequential</span> argument also recognizes the letter a, which will insert an AffixTagger into the backoff chain. If you do not specify the <span class="pre">--affix</span> argument, then it will include one AffixTagger with a 3-character suffix. However, you can change this by specifying one or more <span class="pre">--affix</span> N options, where N should be a positive number for prefixes, and a negative number for suffixes. For example, to train an aubt tagger with 2 AffixTaggers, one that uses a 3 character suffix, and another that uses a 2 character prefix, specify the <span class="pre">--affix</span> argument twice:

python train_tagger.py treebank --sequential aubt --affix -3 --affix 2

The order of the <span class="pre">--affix</span> arguments is the order in which each AffixTagger will be trained and inserted into the backoff chain.

Training Brill Taggers

To train a BrillTagger in a similar fashion to the one trained in Part of Speech Tagging Part 3 – Brill Tagger (using FastBrillTaggerTrainer), use the <span class="pre">--brill</span> argument:

python train_tagger.py treebank --sequential aubt --brill

The default training options are a maximum of 200 rules with a minimum score of 2, but you can change that with the <span class="pre">--max_rules</span> and <span class="pre">--min_score</span> arguments. You can also change the rule template bounds, which defaults to 1, using the <span class="pre">--template_bounds</span> argument.

Training Classifier Based Taggers

Many of the arguments used by train_classifier.py can also be used to train a ClassifierBasedPOSTagger. If you don’t want this tagger to backoff to a sequential backoff tagger, be sure to specify <span class="pre">--sequential</span> ''. Here’s an example for training a NaiveBayesClassifier based tagger, similar to what was shown in Part of Speech Tagging Part 4 – Classifier Taggers:

python train_tagger.py treebank --sequential '' --classifier NaiveBayes

If you do want to backoff to a sequential tagger, be sure to specify a cutoff probability, like so:

python train_tagger.py treebank --sequential ubt --classifier NaiveBayes --cutoff_prob 0.4

Any of the NLTK classification algorithms can be used for the <span class="pre">--classifier</span> argument, such as Maxent or MEGAM, and every algorithm other than NaiveBayes has specific training options that can be customized.

Phonetic Feature Options

You can also include phonetic algorithm features using the following arguments:

<span class="pre">--metaphone</span>:	Use metaphone feature
<span class="pre">--double-metaphone</span>:	Use double metaphone feature
<span class="pre">--soundex</span>:	Use soundex feature
<span class="pre">--nysiis</span>:	Use NYSIIS feature
<span class="pre">--caverphone</span>:	Use caverphone feature

These options create phonetic codes that will be included as features along with the default features used by the ClassifierBasedPOSTagger. The <span class="pre">--double-metaphone</span> algorithm comes from metaphone.py, while all the other phonetic algorithm have been copied from the advas project (which appears to be abandoned).

I created these options after discussions with Michael D Healy about Twitter Linguistics, in which he explained the prevalence of regional spelling variations. These phonetic features may be able to reduce that variation where a tagger is concerned, as slightly different spellings might generate the same phonetic code.

A tagger trained with any of these phonetic features will be an instance of nltk_trainer.tagging.taggers.PhoneticClassifierBasedPOSTagger, which means nltk_trainer must be included in your PYTHONPATH in order to load & use the tagger. The simplest way to do this is to install nltk-trainer using python setup.py install.