This document discusses a research talk on efficiently categorizing text documents using a massively parallel machine learning model. The model was used to classify over two million Japanese newspaper articles into 75 categories using advanced computing resources. Experiments on an IBM server showed that the proposed parallel model effectively categorized large text corpora, comparing different feature extraction methods and classifiers.

1. Efficient Text Categorization Using a Massively Parallel Machine Learning Model
LU Bao-Liang
Department of Computer Science and Engineering
Shanghai Jiao Tong University
blu@cs.sjtu.edu.cn
Abstract: In this talk, we present our recent research progress on categorizing Japanese text documents
based on a massively parallel machine learning model. By using this model, we can deal with the
problem of classifying large-scale document sets with advanced computing resources such as cluster
system and grid computing system. We perform experiments on an IBM P690 sever computer system
to classify Yomiuri Newspaper Corpus, which includes over two millions text documents and 75
different categories. We compare various feature extraction methods and several popular pattern
classifiers such as k-NN and support vector machines on Yomiuri Newspaper Corpus. The simulation
results show the effectiveness of the proposed massively parallel machine learning model for text
categorization.