Scaling up Open Tagging from Tens to Thousands: Comprehension Empowered Attribute Value Extraction from Product Title
Huimin Xu, Meng Wan, Xin Mao, Xinyu Jiang, Man Lan · 2019
Supplementing product information by extracting attribute values from title is a crucial task in e-Commerce domain.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.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.We conduct extensive experiments in real-life datasets.The results show that our model not only outperforms existing state-of-the-art N-ER tagging models, but also is robust and generates promising results for up to 8, 906 attributes.