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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 further
Operates On: all lexical Items.
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:
Resource Data
Description of resource.
$action.getHelper().renderConfluenceMacro("$codeS$body$codeE")
Fields