Hierarchical thematic classification of major conference proceedings

TL;DR

This paper introduces a hierarchical text classification system that uses a weighted similarity function and Bayesian inference to improve topic relevance ranking in conference proceedings.

Contribution

It presents a novel weighted hierarchical similarity function combined with a Bayesian inference approach for hierarchical text classification.

Findings

Outperforms hierarchical SVM, PLSA, and naive Bayes in ranking accuracy.

Uses entropy-based word importance for weighting.

Provides a closed-form EM algorithm for parameter estimation.

Abstract

In this paper, we develop a decision support system for the hierarchical text classification. We consider text collections with a fixed hierarchical structure of topics given by experts in the form of a tree. The system sorts the topics by relevance to a given document. The experts choose one of the most relevant topics to finish the classification. We propose a weighted hierarchical similarity function to calculate topic relevance. The function calculates the similarity of a document and a tree branch. The weights in this function determine word importance. We use the entropy of words to estimate the weights. The proposed hierarchical similarity function formulates a joint hierarchical thematic classification probability model of the document topics, parameters, and hyperparameters. The variational Bayesian inference gives a closed-form EM algorithm. The EM algorithm estimates the parameters and calculates the probability of a topic for a given document. Compared to hierarchical multiclass SVM, hierarchical PLSA with adaptive regularization, and hierarchical naive Bayes, the weighted hierarchical similarity function has better improvement in ranking accuracy in an abstract collection of a major conference EURO and a website collection of industrial companies.