Entity Attribute Relation Extraction with Attribute-Aware Embeddings

Dan Iter, Xiao Yu, Fangtao Li


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
Venues:
DeeLIO | EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
50–55
Language:
URL:
https://www.aclweb.org/anthology/2020.deelio-1.6
DOI:
10.18653/v1/2020.deelio-1.6
Bib Export formats:
BibTeX MODS XML EndNote
PDF:
http://aclanthology.lst.uni-saarland.de/2020.deelio-1.6.pdf