Toward Optimal Feature Selection in Naive Bayes for Text Categorization

Automated feature selection is important for text categorization to reduce the feature size and to speed up the learning process of classifiers. In this paper, we present a novel and efficient feature selection framework based on the Information Theory, which aims to rank the features with their discriminative capacity for classification. We first revisit two information measures: Kullback-Leibler divergence and Jeffreys divergence for binary hypothesis testing, and analyze their asymptotic properties relating to type I and type II errors of a Bayesian classifier. We then introduce a new divergence measure, called Jeffreys-Multi-Hypothesis (JMH) divergence, to measure multi-distribution divergence for multi-class classification. Based on the JMH-divergence, we develop two efficient feature selection methods, termed maximum discrimination ($MD$) and $MD-\chi^2$ methods, for text categorization. The promising results of extensive experiments demonstrate the effectiveness of the proposed approaches.Comment: This paper has been submitted to the IEEE Trans. Knowledge and Data Engineering. 14 pages, 5 figure

A refinement framework for cross language text categorization

Abstract. Cross language text categorization is the task of exploiting labelled documents in a source language (e.g. English) to classify documents in a target language (e.g. Chinese). In this paper, we focus on investigating the use of a bilingual lexicon for cross language text categorization. To this end, we propose a novel refinement framework for cross language text categorization. The framework consists of two stages. In the first stage, a cross language model transfer is proposed to generate initial labels of documents in target language. In the second stage, expectation maximization algorithm based on naive Bayes model is introduced to yield resulting labels of documents. Preliminary experimental results on collected corpora show that the proposed framework is effective

Consistent Text Categorization using Data Augmentation in e-Commerce

The categorization of massive e-Commerce data is a crucial, well-studied task, which is prevalent in industrial settings. In this work, we aim to improve an existing product categorization model that is already in use by a major web company, serving multiple applications. At its core, the product categorization model is a text classification model that takes a product title as an input and outputs the most suitable category out of thousands of available candidates. Upon a closer inspection, we found inconsistencies in the labeling of similar items. For example, minor modifications of the product title pertaining to colors or measurements majorly impacted the model's output. This phenomenon can negatively affect downstream recommendation or search applications, leading to a sub-optimal user experience. To address this issue, we propose a new framework for consistent text categorization. Our goal is to improve the model's consistency while maintaining its production-level performance. We use a semi-supervised approach for data augmentation and presents two different methods for utilizing unlabeled samples. One method relies directly on existing catalogs, while the other uses a generative model. We compare the pros and cons of each approach and present our experimental results

The Bregman Variational Dual-Tree Framework

Graph-based methods provide a powerful tool set for many non-parametric frameworks in Machine Learning. In general, the memory and computational complexity of these methods is quadratic in the number of examples in the data which makes them quickly infeasible for moderate to large scale datasets. A significant effort to find more efficient solutions to the problem has been made in the literature. One of the state-of-the-art methods that has been recently introduced is the Variational Dual-Tree (VDT) framework. Despite some of its unique features, VDT is currently restricted only to Euclidean spaces where the Euclidean distance quantifies the similarity. In this paper, we extend the VDT framework beyond the Euclidean distance to more general Bregman divergences that include the Euclidean distance as a special case. By exploiting the properties of the general Bregman divergence, we show how the new framework can maintain all the pivotal features of the VDT framework and yet significantly improve its performance in non-Euclidean domains. We apply the proposed framework to different text categorization problems and demonstrate its benefits over the original VDT.Comment: Appears in Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence (UAI2013

Concept-Based Automatic Amharic Document Categorization

Along with the continuously growing volume of information resources, there is a growing interest toward better solutions for finding, filtering and organizing these resources. Automatic text categorization can play an important role in a wide variety of flexible, dynamic, and personalized information management tasks. The aim of this research work is to make use of concepts as a way of improving the categorization process for Amharic1 documents. In recent years, ontology-based document categorization method is introduced to solve the problem of document classification. Previous works on keyword-based document categorization miss some important issues of considering semantic relationships between words. In order to resolve the existing problems, this research proposed a framework that automatically categorizes Amharic documents into predefined categories using concepts. The research shows that the use of concepts for an Amharic document categorizer results in 92.9% accuracy