Utilizing Probase in Open Directory Project-based Text Classification
원문 초록
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.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
TokenBreak: Bypassing Text Classification Models Through Token Manipulation
Incremental Sequence Classification with Temporal Consistency
Small Language Models in the Real World: Insights from Industrial Text Classification
Token Masking Improves Transformer-Based Text Classification
KDH-MLTC: Knowledge Distillation for Healthcare Multi-Label Text Classification
Towards Robust Few-Shot Text Classification Using Transformer Architectures and Dual Loss Strategies
토론
이 논문과 연결된 포럼 글이 아직 없습니다.