  Hierarchical text classification has many real-world applications. However,
labeling a large number of documents is costly. In practice, we can use
semi-supervised learning or weakly supervised learning (e.g., dataless
classification) to reduce the labeling cost. In this paper, we propose a path
cost-sensitive learning algorithm to utilize the structural information and
further make use of unlabeled and weakly-labeled data. We use a generative
model to leverage the large amount of unlabeled data and introduce path
constraints into the learning algorithm to incorporate the structural
information of the class hierarchy. The posterior probabilities of both
unlabeled and weakly labeled data can be incorporated with path-dependent
scores. Since we put a structure-sensitive cost to the learning algorithm to
constrain the classification consistent with the class hierarchy and do not
need to reconstruct the feature vectors for different structures, we can
significantly reduce the computational cost compared to structural output
learning. Experimental results on two hierarchical text classification
benchmarks show that our approach is not only effective but also efficient to
handle the semi-supervised and weakly supervised hierarchical text
classification.
