class BrillTagger(TaggerI): (source)
Constructor: BrillTagger(initial_tagger, rules, training_stats)
Brill's transformational rule-based tagger. Brill taggers use an initial tagger (such as tag.DefaultTagger) to assign an initial tag sequence to a text; and then apply an ordered list of transformational rules to correct the tags of individual tokens. These transformation rules are specified by the TagRule interface.
Brill taggers can be created directly, from an initial tagger and a list of transformational rules; but more often, Brill taggers are created by learning rules from a training corpus, using one of the TaggerTrainers available.
| Class Method | decode |
Undocumented |
| Method | __init__ |
No summary |
| Method | batch |
Tags by applying each rule to the entire corpus (rather than all rules to a single sequence). The point is to collect statistics on the test set for individual rules. |
| Method | encode |
Undocumented |
| Method | print |
Print a list of all templates, ranked according to efficiency. |
| Method | rules |
Return the ordered list of transformation rules that this tagger has learnt |
| Method | tag |
Undocumented |
| Method | train |
Return a named statistic collected during training, or a dictionary of all available statistics if no name given |
| Class Variable | json |
Undocumented |
| Instance Variable | _initial |
Undocumented |
| Instance Variable | _rules |
Undocumented |
| Instance Variable | _training |
Undocumented |
| Parameters | |
| initial | The initial tagger |
| rules:list(TagRule) | An ordered list of transformation rules that should be used to correct the initial tagging. |
| training | A dictionary of statistics collected during training, for possible later use |
Tags by applying each rule to the entire corpus (rather than all rules to a single sequence). The point is to collect statistics on the test set for individual rules.
NOTE: This is inefficient (does not build any index, so will traverse the entire corpus N times for N rules) -- usually you would not care about statistics for individual rules and thus use batch_tag() instead
| Parameters | |
| sequences:list of list of strings | lists of token sequences (sentences, in some applications) to be tagged |
| gold:list of list of strings | the gold standard |
| Returns | |
| tuple of (tagged_sequences, ordered list of rule scores (one for each rule)) | |
Print a list of all templates, ranked according to efficiency.
If test_stats is available, the templates are ranked according to their relative contribution (summed for all rules created from a given template, weighted by score) to the performance on the test set. If no test_stats, then statistics collected during training are used instead. There is also an unweighted measure (just counting the rules). This is less informative, though, as many low-score rules will appear towards end of training.
| Parameters | |
| test | dictionary of statistics collected during testing |
| printunused:bool | if True, print a list of all unused templates |
| Returns | |
| None | None |
Return the ordered list of transformation rules that this tagger has learnt
| Returns | |
| list of Rules | the ordered list of transformation rules that correct the initial tagging |