Scaling Up Open Tagging from Tens to Thousands: ComprehensionEmpowered Attribute Value Extraction from Product Title
Abstract
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 NER tagging models, but also is robust and generates promising results for up to 8, 906 attributes.