Extracting structured knowledge from product profiles is crucial for various applications in e-Commerce. State-of-the-art approaches for knowledge extraction were each designed for a single category of product, and thus do not apply to real-life e-Commerce scenarios, which often contain thousands of diverse categories. This paper proposes TXtract, a taxonomy-aware knowledge extraction model that applies to thousands of product categories organized in a hierarchical taxonomy. Through category conditional self-attention and multi-task learning, our approach is both scalable, as it trains a single model for thousands of categories, and effective, as it extracts category-specific attribute values. Experiments on products from a taxonomy with 4,000 categories show that TXtract outperforms state-of-the-art approaches by up to 10% in F1 and 15% in coverage across all categories.
@article{arxiv.2004.13852,
title = {TXtract: Taxonomy-Aware Knowledge Extraction for Thousands of Product Categories},
author = {Giannis Karamanolakis and Jun Ma and Xin Luna Dong},
journal= {arXiv preprint arXiv:2004.13852},
year = {2020}
}