![]() ![]() ![]() Association for Computational Linguistics. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 5214–5223, Florence, Italy. ![]() Scaling up Open Tagging from Tens to Thousands: Comprehension Empowered Attribute Value Extraction from Product Title. Anthology ID: P19-1514 Volume: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics Month: July Year: 2019 Address: Florence, Italy Venue: ACL SIG: Publisher: Association for Computational Linguistics Note: Pages: 5214–5223 Language: URL: DOI: 10.18653/v1/P19-1514 Bibkey: xu-etal-2019-scaling Cite (ACL): Huimin Xu, Wenting Wang, Xin Mao, Xinyu Jiang, and Man Lan. The results show that our model not only outperforms existing state-of-the-art NER tagging models, but also is robust and generates promising results for up to 8,906 attributes. We conduct extensive experiments in real-life datasets. In this work, we propose a novel approach to support value extraction scaling up to thousands of attributes without losing performance: (1) We propose to regard attribute as a query and adopt only one global set of BIO tags for any attributes to reduce the burden of attribute tag or model explosion (2) We explicitly model the semantic representations for attribute and title, and develop an attention mechanism to capture the interactive semantic relations in-between to enforce our framework to be attribute comprehensive. Previous studies treat each attribute only as an entity type and build one set of NER tags (e.g., BIO) for each of them, leading to a scalability issue which unfits to the large sized attribute system in real world e-Commerce. Abstract Supplementing product information by extracting attribute values from title is a crucial task in e-Commerce domain. ![]()
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