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Creates a bag of words / tfidf tag with the vector information for the document/text_block/sentence. Accumulates the vector until the engine cannot read any furtherOperates On:  Lexical Items with TOKEN and possibly other flags as specified below.

Stage can only be used as part of a manual pipeline or a base pipeline

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

  • vocabulary ( type=string | required ) - JSON map resource in which the vocabulary is stored
  • vectorType ( type=string | default=BOW | required ) - Type of algorithm to use then building the vector, can be either BOW or TF_IDF
  • datasetId ( type=string | required ) - Dataset ID from which the vocabulary was extracted
  • min ( type=integer | default=1 | required ) - Minimum number of tokens to match
  • max ( type=integer | default=2 | required ) - Maximum number of tokens to match


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

Example Output

In this example the stage load a predefined vocabulary to generate a vector for the sentence using BOW, the same is done but using TF_IDF $action.getHelper().renderConfluenceMacro("$codeS$body$codeE")

Output Flags

Lex-Item Flags:

  • WEIGHT_VECTOR - Identifies the tag as a weight vector representation of a sentence

Resource Data

Description of resource.

Resource Format

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

  • Multiple entries can have the same pattern. If the pattern is matched, then it will be tagged with multiple (ambiguous) entry IDs.
  • Additional fielded data can be added to the record; as needed by downstream processes.

Fields

  • display ( type=string | required ) - What to show the user when browsing this entity
  • tag ( type=string | required ) - Tag which will identify any match in the graph, as an interpretation

    • These will all be added to the interpretation graph with the SEMANTIC_TAG flag.

      Tags are hierarchical representations of the same intent. For example, {city} → {administrative-area} → {geographical-area}

  • pattern ( type=string | required ) - Pattern to match in the content

  • _id ( type=string | required ) - Identifies the entity by unique ID. This identifier must be unique across all entries (across all dictionaries).

  • confAdjust ( type=boolean | required ) - Adjustment factor to apply to the confidence value of 0.0 to 2.0

    • This is the confidence of the entry, in comparison to all of the other entries. (Essentially, the likelihood that this entity will be randomly encountered.)
    • 0.0 to < 1.0  decreases confidence value
    • 1.0 confidence value remains the same
    • > 1.0 to  2.0 increases confidence value
  • updatedAt ( type=date epoch | required ) - Date in milliseconds of the last time the entry was updated
  • createdAt ( type=date epoch | required ) - Date in milliseconds of the creation time of the entry



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