Recently I've been doing a lot of data annotation. I've been working on a project to extract a shopping list from a screenshot that requires a custom NLP model to be utilized. Of course to build a custom NLP model requires a fair amount of annotated data, which is a tedious task to undertake. To help with the data annotation I built a lightweight Python class, AnnotateHelper, to interact with in a Jupyter Notebook to help annotate a document.

To get started all you have to do is initialize the AnnotateHelper class with the text you want to annotate.

annotate_obj = AnnotateHelper(<text_to_annotate>)

One the AnnotateHelper class is initialized you can utilize it's methods to help annotate the document. If you have an existing NER model you can leverage that model to seed the initial annotations with the suggest_entities method. Utilizing an existing model gives you a place to start with annotating so you don't have to grind through annotating each entity individually.

Dropping an annotation from a document can be done with the drop_entity method by passing the entity_id that you want to drop.

Entities can be added with the entity_span method. Given a phrase entity_span will search the document for occurrences of the phrase and will walkthrough the annotation for each of those occurrences.

Finally, the save method saves the class instance for you to incorporate in your NER model.

My use case was focused on shorter length documents the size of a long text message. I'm not sure how well it would work with longer documents, but the format does seem to work well with the shorter documents I'm dealing with. The initial features I've added are pretty sparse. As I use this utility more I'll expand the functionality.