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 further

Operates On:  all lexical Items.

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

This stage is disabled in version 1.2.2

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
  • TOKEN - Identifies that the Lex-Items produced by this stage are tokens and not text blocks.

Vertex Flags:

No vertices are created in this stage


Resource Data

Description of resource.

Resource Format

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

Fields

  • count ( type=integer | required ) - number of time the word appeared
  • docsPerTerm ( type=integer | required ) - Number of document in which the word appeared
  • datasetId ( type=string | required ) - dataset ID in from which the vocabulary was extracted
  • tokenCount ( type=integer | required ) - number of tokens for the word
  • docCount ( type=integer | required ) - number of documents in the dataset

  • word ( type=string | required ) - word of the vocabulary