Utilizing Probase in Open Directory Project-based Text Classification
This addresses a domain-specific problem in text classification for researchers or practitioners using ODP, but it is incremental as it builds on existing methods.
The paper tackled the limited entity coverage in Open Directory Project (ODP) for text classification by enriching ODP categories with Probase entities, resulting in a significant improvement over state-of-the-art techniques.
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.