An experiment about re-implementing supervised learning models based on shallow neural network approaches (e.g. fastText) with some additional exclusive features and nice API. Written in Python and fully compatible with Scikit-learn.
Documents can be structured, made of different sections (title, body, tags (tags positions are not significant, but they dependent on the document body)), learned independently
Implementation:
the input data is a dict of the sections
in the learning algorithm: concatenate labels to words according to their section
Documents can be structured, made of different sections (title, body, tags (tags positions are not significant, but they dependent on the document body)), learned independently
Implementation: