Looks up matches to regular expressions in a dictionary within a single token and then tags the match with one or more semantic tags as an alternative representation.
Operates On: Lexical Items with TOKEN and possibly other flags as specified below.
All possibilities are tagged, including overlaps and sub-patterns, with the expectation that later disambiguation stages will choose which tags are the correct interpretation.
$action.getHelper().renderConfluenceMacro("$codeS$body$codeE")
In the following example, "number" is in the dictionary as a regex for using "[0-9]+" and "[0-9]+\\.[0-9]+" : $action.getHelper().renderConfluenceMacro("$codeS$body$codeE")
Notice that on the example for Regex Pattern Stage the "self-name" tag would have a potential match with "What's your name". However, the Simple Regex Stage does not lookup for matches beyond a single token (as the Regex Patter Stage does).
No vertices are created in this stage
The regex pattern must have an "pattern dictionary" (a string to JSON map) which is a list of JSON records, indexed by entity ID. In addition, there may also be a pattern map and a token index.
The only required file is the pattern dictionary. It is a series of JSON records, typically indexed by entity ID.
Each JSON record represents an entity. The format is as follows: $action.getHelper().renderConfluenceMacro("$codeS$body$codeE")Notes
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