Acclimatizing Taxonomic Semantics for Hierarchical Content Classification

Hierarchical models have been shown to be effective in con-tent classification. However, the authors 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, they elucidate why a given semantics-based hierarchy does not work well in con-tent classification, and how it could be improved for accurate hierarchical classification. With these understandings, they propose effective localized solutions that modify the given taxonomy for accurate classification.