Learning Context for Text Categorization

TL;DR

This paper introduces a novel text categorization method that leverages context discovery and relation extraction, significantly enhancing performance on standard and domain-specific datasets.

Contribution

It presents a new approach combining relation extraction with context discovery for improved text document categorization.

Findings

Context learning improves categorization accuracy

Effective on Reuters 21578 dataset

Works well with sports domain data

Abstract

This paper describes our work which is based on discovering context for text document categorization. The document categorization approach is derived from a combination of a learning paradigm known as relation extraction and an technique known as context discovery. We demonstrate the effectiveness of our categorization approach using reuters 21578 dataset and synthetic real world data from sports domain. Our experimental results indicate that the learned context greatly improves the categorization performance as compared to traditional categorization approaches.