Multi-label Text Categorization with Joint Learning Predictions-as-Features Method
Li Li, Houfeng Wang, Xu Song Sun, Baobao Chang, Shi Zhao, Lei Sha · 2015
Multi-label text categorization is a type of text categorization, where each document is assigned to one or more categories.Recently, a series of methods have been developed, which train a classifier for each label, organize the classifiers in a partially ordered structure and take predictions produced by the former classifiers as the latter classifiers' features.These predictions-asfeatures style methods model high order label dependencies and obtain high performance.Nevertheless, the predictionsas-features methods suffer a drawback.When training a classifier for one label, the predictions-as-features methods can model dependencies between former labels and the current label, but they can't model dependencies between the current label and the latter labels.To address this problem, we propose a novel joint learning algorithm that allows the feedbacks to be propagated from the classifiers for latter labels to the classifier for the current label.We conduct experiments using real-world textual data sets, and these experiments illustrate the predictions-as-features models trained by our algorithm outperform the original models.