Incremental Reranking for Hierarchical Text Classification

The top-down method is efficient and commonly used in hierarchical text classification. Its main drawback is the error propagation from the higher to the lower nodes. To address this issue we propose an efficient incremental reranking model of the top-down classifier decisions. We build a multiclassifier for each hierarchy node, constituted by the latter and its children. Then we generate several classification hypotheses with such classifiers and rerank them to select the best one. Our rerankers exploit category dependencies, which allow them to recover from the multiclassifier errors whereas their application in top-down fashion results in high efficiency. The experimentation on Reuters Corpus Volume 1 (RCV1) shows that our incremental reranking is as accurate as global rerankers but at least one magnitude order faster.