Novel top-down methods for Hierarchical Text Classification

Novel methods for text preprocessing and classification

Enhance Top-down method with Meta-Classification for Very Large-scale Hierarchical Classification

Recent large-scale hierarchical classification tasks typically have tens of thousands of classes as well as a large number of samples, for which the dominant solution is the top-down method due to computational complexity. However, the top-down method suffers from accuracy deficiency, that is, its accuracy is generally lower than that of the flat approach of 1-vs-Rest. In this paper, we employ ...

Hierarchical Bayes for Text Classification

Naive Bayes models have been very popular in several classification tasks. In this paper we study the application of these models to classification tasks where the data is sparse i.e., a large number of possible outcomes do not appear in the data. Traditionally point estimates of the model parameters and in particular, point estimates based on the Laplace’s rule have been popular for such spars...

Hierarchical Text Classification using Methods from Machine Learning

Due to the permantently growing amount of textual data, automatic methods for organizing the data are needed. Automatic text classification is one of this methods. It automatically assigns documents to a set of classes based on the textual content of the document. Normally, the set of classes is hierarchically structured but today’s classification approaches ignore hierarchical structures, ther...

Chapter 14 HIERARCHICAL TEXT CLASSIFICATION METHODS AND THEIR SPECIFICATION

Hierarchical text classification refers to assigning text documents to the categories in a given category tree based on their content. With large number of categories organized as a tree, hierarchical text classification helps users to find information more quickly and accurately. Nevertheless, hierarchical text classification methods in the past have often been constructed in a proprietary man...