Utilizing Probase in Open Directory Project-based Text Classification

TL;DR

This paper enhances Open Directory Project-based text classification by integrating Probase entities, significantly improving classification accuracy through semantic enrichment of categories.

Contribution

It introduces a novel method to incorporate Probase entities into ODP categories using concept vectors, improving semantic representation for classification.

Findings

Significant improvement over state-of-the-art methods

Effective semantic relevance measurement between categories and entities

Enhanced category semantics lead to better classification performance

Abstract

Open Directory Project (ODP) has been successfully utilized in text classification due to its representation ability of various categories. However, ODP includes a limited number of entities, which play an important role in classification tasks. In this paper, we enrich the semantics of ODP categories with Probase entities. To effectively incorporate Probase entities in ODP categories, we first represent each ODP category and Probase entity in terms of concepts. Next, we measure the semantic relevance between an ODP category and a Probase entity based on the concept vector. Finally, we use Probase entity to enrich the semantics of the ODP categories. Our experimental results show that the proposed methodology exhibits a significant improvement over state-of-the-art techniques in the ODP-based text classification.