تقرير
Leveraging Knowledge Graph Embeddings to Enhance Contextual Representations for Relation Extraction
| العنوان: | Leveraging Knowledge Graph Embeddings to Enhance Contextual Representations for Relation Extraction |
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| المؤلفون: | Laleye, Fréjus A. A., Rakotoson, Loïc, Massip, Sylvain |
| سنة النشر: | 2023 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computation and Language, Computer Science - Machine Learning |
| الوصف: | Relation extraction task is a crucial and challenging aspect of Natural Language Processing. Several methods have surfaced as of late, exhibiting notable performance in addressing the task; however, most of these approaches rely on vast amounts of data from large-scale knowledge graphs or language models pretrained on voluminous corpora. In this paper, we hone in on the effective utilization of solely the knowledge supplied by a corpus to create a high-performing model. Our objective is to showcase that by leveraging the hierarchical structure and relational distribution of entities within a corpus without introducing external knowledge, a relation extraction model can achieve significantly enhanced performance. We therefore proposed a relation extraction approach based on the incorporation of pretrained knowledge graph embeddings at the corpus scale into the sentence-level contextual representation. We conducted a series of experiments which revealed promising and very interesting results for our proposed approach.The obtained results demonstrated an outperformance of our method compared to context-based relation extraction models. Comment: 15 pages, 1 figures, The 17th International Conference on Document Analysis and Recognition |
| نوع الوثيقة: | Working Paper |
| DOI: | 10.1007/978-3-031-41501-2_2 |
| URL الوصول: | http://arxiv.org/abs/2306.04203 |
| رقم الأكسشن: | edsarx.2306.04203 |
| قاعدة البيانات: | arXiv |
| DOI: | 10.1007/978-3-031-41501-2_2 |
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