Multiple issues with character based annotation

[ines (Ines Montani)]

Thanks for the report!

@Silent

During annotation, if I select across an entire word, but without selecting the trailing space, it appears to have selected the word on the UI. However when releasing mouse the last character is not selected.

I'll look into that! I remember there being some inconsistencies between how Chrome and Firefox handle highlighting with trailing whitespace, but from what I remember, Firefox was actually most consistent here. Does this only occur with characters that are followed by actual whitespace?

Do you have an example text that shows the problem? I've been trying to reproduce this but I can't seem to make the UI produce this type ouf output or zero-length spans

@Silent

During annotation, if I wish to select something that starts from the very first character, I sometimes do a backward selection (from the end of span) because it's faster and do not need to do a pixel perfect selection. However if I move beyond the first character and out of the drop shadowed part of the annotation UI then release the mouse, it does not register as a mouse release.

That's going to be tricky to work around and I don't have a perfect solution for this at the moment. One idea could be to add some spacing around the main text container (.prodigy-spans) to give you more space for your cursor. You could also try this out by using global_css and adding some padding on the left.

@Silent

During annotation if I first select 1 character, and without releasing mouse I reverse that selection, now releasing mouse can cause a zero character selection. It cause a permanent bug during this session as there is no way to cancel or deselect that annotation without restart.

That's definitely a bug, thanks! Zero-character spans shouldn't be valid, so we'll fix this to prevent them from being locked in.

@Silent

There is no hightlight-chars for ner.teach or ner.correct. Can this be added to the standard recipes so I can train character based model with active learning?

This wouldn't really work. If you're training a model that predicts token-based tags, e.g. a named entity recognizer, the spans you provide need to map to valid tokens. So it doesn't really make sense to annotate characters here because you'll very easily end up with spans that the model can't learn from.