document: 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 hierarchical 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 .
doc_bio_tags: O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O