The name predictor stage uses OpenNLP's NameFinder to load Name Entity Recognizer models and tag tokens that match entities based on the model given a certain threshold of accuracy.

Operates On:  Lexical Items with TOKEN within TEXT_BLOCK_SPLIT and SENTENCE_SPLIT vertex flags.

Library: saga-name-trainer-stage

Stage is a Recognizer for Saga Solution, and can also be used as part of a manual pipeline or a base pipeline

Models can be trained directly with OpenNLP tools or from the Saga UI. See Name Entity Recognizer in the User Manual for more information on how to create a model using Saga.


Generic Configuration Parameters

  • boundaryFlags ( type=string array | optional ) - List of vertex flags that indicate the beginning and end of a text block.
    Tokens to process must be inside two vertices marked with this flag (e.g ["TEXT_BLOCK_SPLIT"])
  • skipFlags ( type=string array | optional ) - Flags to be skipped by this stage.
    Tokens marked with this flag will be ignored by this stage, and no processing will be performed.
  • requiredFlags ( type=string array | optional ) - Lex items flags required by every token to be processed.
    Tokens need to have all of the specified flags in order to be processed.
  • atLeastOneFlag ( type=string array | optional ) - Lex items flags needed by every token to be processed.
    Tokens will need at least one of the flags specified in this array.
  • confidenceAdjustment ( type=double | default=1 | required ) - Adjustment factor to apply to the confidence value of 0.0 to 2.0 from (Applies for every pattern match).
    • 0.0 to < 1.0  decreases confidence value
    • 1.0 confidence value remains the same
    • > 1.0 to  2.0 increases confidence value
  • debug ( type=boolean | default=false | optional ) - Enable all debug log functionality for the stage, if any.
  • enable ( type=boolean | default=true | optional ) - Indicates if the current stage should be consider for the Pipeline Manager
    • Only applies for automatic pipeline building

Configuration Parameters

  • tagWith ( type=string | default=match | optional ) - if used with automatic pipeline creation, it assigns the tag to which the recognizer belongs to.
  • prob ( type=double | default=0.95 | optional ) - Probability threshold. Will only tag sentences that match better or equal to prob.
  • model ( type=string | optional ) - File location of the model.
  • normalize ( type=string array | optional ) - List of Tags used to normalize the text
    • For example, let's say you want to normalize all different numbers in the text. You can create a "Numeric" tag using the numeric recognizer, that way each different number will me normalized to "{Numeric}".


$action.getHelper().renderConfluenceMacro("$codeS$body$codeE")

Example Output

$action.getHelper().renderConfluenceMacro("$codeS$body$codeE")

Output Flags

Lex-Item Flags

  • SEMANTIC_TAG - Identifies all lexical items that are semantic tags.
  • ML_TOKEN - Placed on tokens that find a match of a named entity from the model.

Vertex Flags

No vertices are created in this stage