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Operates On:  Lexical Items with TOKEN and TEXT_BLOCK.

Settings

• Parameter
  summary Execute after these tags. default false name Dependency Tags type boolean

• Parameter
  summary Tags (flagged as SEMANTIC_TAG) are normalized. default false name normalizeTags type boolean

  • Whenever a tag has field (metadata) named "display" it value is used to normalize the tag, if the tag does not have "display" value then the name of tag is used, e.g. {costumer} = costumer.

• Parameter
  summary Includes up to two fields in the metadata of the new tag, the values are "before" and "after". default false name includeContext type boolean

  • The "before" value includes previous blocks (or lines) from the current text being process, using the "Boundary Flags" from the general settings to delimit the blocks, existing in the current graph, up to 2 blocks if possible. The "after" field include up to two blocks after the processed text.

• Parameter
  summary List of flags used to skip blocks (or lines) that should not be process by the stage. name skipVertexFlags

  • The flagged vertex marks the start of the block and the end is set by the "Boundary Flags" value in the settings.

Configuration Parameters

• Parameter
  summary Model name registered in the python bridge name modelName required true

• Parameter
  summary Model version registered in the python wrapper to query default latest name modelVersion

• Parameter
  summary Model method to call for the model name modelMethod required true

• Parameter
  summary Python server communication hostname default localhost name hostname

• Parameter
  summary Python server communication port default 5000 name port

• Parameter
  summary Percentage of similarity to be accepted by the recognizer. A higher threshold would mean closer results but less similarities found, a lower threshold would find more similarities but less closer results. default 0.6 name Match Threshold type double required true

"dependencyTags": [],
"includeContext": false,
"modelName": "lsi",
"modelVersion": "1",
"modelMethod": "predict",
"normalizeTags": false,
"threshold": 0.6,
"hostname": "localhost",
"port": 5000

Example Output

V----[THOMAS B. SEEKER]-----V-------[1234 Northern Star Circle]-------V-------[Baltimore, MD 12345]--------V
^---[THOMAS B]---V-[SEEKER]-^-[1234]-V-[Northern]-V-[Star]-V-[Circle]-^---[Baltimore,]----V-[MD]-V-[12345]-^
^-[THOMAS]-V-[B]-^                   ^-[northern]-^-[star]-^-[circle]-^-[Baltimore]-V-[,]-^-[md]-^         ^
^-[thomas]-^-[b]-^-[SEEKER]-^-----------------[{lsi}]-----------------^---[baltimore,]----^                ^
^----[{lsi}]-----^-[seeker]-^                                         ^-[baltimore]-^                      ^
                 ^-[{lsi}]--^                                         ^--------------[{lsi}]---------------^

Output Flags

Lex-Item Flags:

• WEIGHT_VECTOR - Identifies the tag as a weight vector representation of a sentence
• ML_PREDICT- Result from a machine learning algorithm for prediction
• ML_CLASSIFY- Result from a machine learning algorithm for classification
• ML_REGRESS- Result from a machine learning algorithm for regression
• SEMANTIC_TAG - Identifies all lexical items that are semantic tags.