Multi-label Text Categorization with Joint Learning Predictions-as-Features Method

Multi-label text categorization is a type of text categorization, where each document is assigned to one or more categories. Re-cently, a series of methods have been de-veloped, which train a classifier for each label, organize the classifiers in a partially ordered structure and take predictions pro-duced by the former classifiers as the latter classifiers ’ features. These predictions-as-features style methods model high order label dependencies and obtain high per-formance. Nevertheless, the predictions-as-features methods suffer a drawback. When training a classifier for one label, the predictions-as-features methods can mod-el dependencies between former labels and the current label, but they can’t model de-pendencies between the current label and the latter labels. To address this problem, we propose a novel joint learning algorith-m that allows the feedbacks to be propa-gated from the classifiers for latter labels to the classifier for the current label. We conduct experiments using real-world tex-tual data sets, and these experiments il-lustrate the predictions-as-features models trained by our algorithm outperform the o-riginal models.