The sentence classifier stage uses OpenNLP's DocumentCategorizer to load classification models and tag sentences that match the binary classification model (is or isn't of a certain category) given a certain threshold of accuracy.
Operates On: Lexical Items with TOKEN within TEXT_BLOCK_SPLIT or SENTENCE_SPLIT vertex flags.
Library: saga-classification-trainer-stage
Models can be trained directly with OpenNLP tools or from the Saga UI. See Classifier Recognizer in the User Manual for more information on how to create a model using Saga.
The stage will use the boundaryFlags specified to split the input text in sentences. All text between boundaries will be considered a sentence and will be evaluated separately by the classifier.
Generic Configuration Parameters
Tokens to process must be inside two vertices marked with this flag (e.g ["TEXT_BLOCK_SPLIT"])
Tokens marked with this flag will be ignored by this stage, and no processing will be performed.
Tokens need to have all of the specified flags in order to be processed.
Tokens will need at least one of the flags specified in this array.
$action.getHelper().renderConfluenceMacro("$codeS$body$codeE")
Example Output
For this case a sentence breaker stage was configured before the classifier stage. tagWith value is animal-incident for this example
$action.getHelper().renderConfluenceMacro("$codeS$body$codeE")
No vertices are created in this stage.