Hierarchical Semi-supervised Classification with Incomplete Class Hierarchies
Bhavana Bharat Dalvi, Aditya Mishra, William W. Cohen · 2016
In an entity classification task, topic or concept hierarchies are often incomplete. Previous work by Dalvi et al. [12] has showed that in non-hierarchical semi-supervised classification tasks, the presence of such unanticipated classes can cause semantic drift for seeded classes. The Exploratory learning [12] method was proposed to solve this problem; however it is limited to the flat classification task. This paper builds such exploratory learning methods for hierarchical classification tasks.