-
Train a tagger:
- java -jar SemiSupervisedTagger.jar train -input [input-file] -model [model-file]
-
Other Options:
* -dev [dev-file] dev file address * -cluster [cluster-file] brown cluster file address * -viterbi if you want to use Viterbi decoding (default: beam decoding) * -update:[mode] for beam training; three #modes: max_viol, early, standard (default: max_viol) * -delim [delim] put delimiter string in [delim] for word tag separator (default _) e.g. -delim / * beam:[#b] put a number [#b] for beam size (default:5); e.g. beam:10 * iter:[#i] put a number [#i] for training iterations (default:20); e.g. iter:10 * NOTE: in every iteration the model file for that iteration will have the format [model-file].iter_#iter e.g. model.iter_3
-
- java -jar SemiSupervisedTagger.jar train -input [input-file] -model [model-file]
-
Tag a file:
- java -jar SemiSupervisedTagger.jar tag -input [input-file] -model [model-file] -output [output-file]
- Other Options:
- -delim [delim] put delimiter string in [delim] for word tag separator (default _) e.g. -delim /
- java -jar SemiSupervisedTagger.jar tag -input [input-file] -model [model-file] -output [output-file]
-
Tag a partially tagged file:
- java -jar SemiSupervisedTagger.jar partial_tag -input [input-file] -model [model-file] -output [output-file]
- For words with no tag information, put *** as the tag; e.g. After_IN our_*** discussion_*** ._.
- Other Options:
- -delim [delim] put delimiter string in [delim] for word tag separator (default _) e.g. -delim /
- java -jar SemiSupervisedTagger.jar partial_tag -input [input-file] -model [model-file] -output [output-file]