Abstract
Entity-attribute relations are a fundamental component for building large-scale knowledge bases, which are widely employed in modern search engines. However, most such knowledge bases are manually curated, covering only a small fraction of all attributes, even for common entities. To improve the precision of model-based entity-attribute extraction, we propose attribute-aware embeddings, which embeds entities and attributes in the same space by the similarity of their attributes. Our model, EANET, learns these embeddings by representing entities as a weighted sum of their attributes and concatenates these embeddings to mention level features. EANET achieves up to 91% classification accuracy, outperforming strong baselines and achieves 83% precision on manually labeled high confidence extractions, outperforming Biperpedia (Gupta et al., 2014), a previous state-of-the-art for large scale entity-attribute extraction.- Anthology ID:
- 2020.deelio-1.6
- Volume:
- Proceedings of Deep Learning Inside Out (DeeLIO): The First Workshop on Knowledge Extraction and Integration for Deep Learning Architectures
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Venue:
- DeeLIO
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 50–55
- Language:
- URL:
- https://aclanthology.org/2020.deelio-1.6
- DOI:
- 10.18653/v1/2020.deelio-1.6
- Cite (ACL):
- Dan Iter, Xiao Yu, and Fangtao Li. 2020. Entity Attribute Relation Extraction with Attribute-Aware Embeddings. In Proceedings of Deep Learning Inside Out (DeeLIO): The First Workshop on Knowledge Extraction and Integration for Deep Learning Architectures, pages 50–55, Online. Association for Computational Linguistics.
- Cite (Informal):
- Entity Attribute Relation Extraction with Attribute-Aware Embeddings (Iter et al., DeeLIO 2020)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2020.deelio-1.6.pdf