Global Model for Hierarchical Multi-Label Text Classification
Yugo Murawaki · International Joint Conference on Natural Language Processing · 2013
The main challenge in hierarchical multilabel text classification is how to leverage hierarchically organized labels. In this paper, we propose to exploit dependencies among multiple labels to be output, which has been left unused in previous studies. To do this, we first formalize this task as a structured prediction problem and propose (1) a global model that jointly outputs multiple labels and (2) a decoding algorithm for it that finds an exact solution with dynamic programming. We then introduce features that capture inter-label dependencies. Experiments show that these features improve performance while reducing the model size.