Acclimatizing taxonomic semantics for hierarchical content classification

Hierarchical models have been shown to be effective in content classification. However, we observe through empirical study that the performance of a hierarchical model varies with given taxonomies; even a semantically sound taxonomy has potential to change its structure for better classification. By scrutinizing typical cases, we elucidate why a given semantics-based hierarchy does not work well in content classification, and how it could be improved for accurate hierarchical classification. With these understandings, we propose effective localized solutions that modify the given taxonomy for accurate classification. We conduct extensive experiments on both toy and real-world data sets, report improved performance and interesting findings, and provide further analysis of algorithmic issues such as time complexity, robustness, and sensitivity to the number of features.