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Using NLP Suggestions

The editor can suggest the constituents of an act frame — actor, action, object, recipient — directly from Dutch source text, using a machine-learning model. This removes the manual first step of deciding which words play which role.

When to use it

NLP suggestions are most useful when you are starting an act on a Dutch sentence and want a head start on identifying its parts. The feature is entirely optional; you can interpret any source without it.

Dutch text only

The underlying model is trained on Dutch normative text. Suggestions on text in other languages are unreliable. Run it on selected sentences or fragments, not on an entire document — very long inputs can exceed the model's token limit.

How to use it

  1. Work on a selected Dutch sentence in the source panel.
  2. Request suggestions for that sentence.
  3. The model returns each word labelled as Actor, Action, Object, Recipient, or none.
  4. The editor surfaces these suggestions so you can turn them into facts and place them into the matching roles of an act.
  5. Review every suggestion. Accept the ones that are right, adjust the boundaries where the model over- or under-selected, and ignore anything incorrect.

When the editor creates an agent fact from a suggestion, it records the model's recommended role as a comment on that fact, so later reviewers can see where the classification came from.

Suggestions are a draft, not an answer

The model is a labelling aid, not an authority on the law. It does not understand claim-duty relations, preconditions, or fact subdivisions — those remain your judgement. Treat its output as a fast first draft of an act's roles that you then verify against the text.

For the technical details of the model and service, see NLP Assistance and Backend & API services.