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Semantic tags identify interpretations of (typically) semantic interpretations of sections of the content. This can include anything from entities (like {person}, {place}, etc.) to full sentence interpretation (as in {person-fact-request}, {restrictive-covenant-term}, {language-fluency-statement}, etc.) or possibly more.
Unlike flags (see above), the Language Processing Toolkit does not pre-define any semantic tags. Instead, semantic tags are determined based on the requirements of the text to be processed.
Specifically:
A key philosophy of this toolkit is that ambiguity is embraced rather than dreaded. To this end, the system will generate all possible semantic tags, including many and various ambiguous alternatives.
All lexical items will have a confidence value, which describe the confidence of the interpretation. This is key for semantic tags where the confidence value can initially come from external sources (e.g. the likelyhood of a entity occurring randomly) and then will build up based on context and how the entity participates in larger patterns.
In addition, patterns can be generated by statistical techniques and then entered into the system. Systems which generate patterns in this way are encouraged to include a confidence value which then is then combined with the confidence of the supporting parts to generate a confidence value for every interpretation.
The output of the processing engine will be an interpretation graph with confidence values.