I have a classification problem to solve, that seems to be common but I am struggling to find the name of this task and the best way to model this problem.
Suppose I have a series of events that are sequential in time.
2 Jan - I matched with a nice girl on Tinder - ACTION_TYPE = SOCIAL_EVENT
5 Jan - I meet with her, it was nice - ACTION_TYPE = SOCIAL_EVENT
8 Jan - I just got accept to a new job. I will meet my boss tomorrow- ACTION_TYPE = PROFESSIONAL_EVENT
10 Jan - I meet with her, it was nice - ACTION_TYPE = PROFESSIONAL_EVENT
It is supervised learning, where I have correctly tagged timelines to train. But during prediction, I have to tag every single event.
I started with a text classification for the text, but I can not distinguish between the events on " 5 Jan" and " 10 Jan".
My instinct is to combine this problem with a sequence tagging, with a CRF layer at the end. But it would be nice if you could look at other possible solutions in the literature.
How would I model this problem? Is this problem known in the literature, and if so, how can I find it?