Towards Open-World Product Attribute Mining: A Lightly-Supervised Approach
This addresses the challenge of automating attribute mining for e-commerce platforms without extensive human labeling, though it is incremental as it builds on existing resources and methods.
The paper tackles the problem of extracting open-world product attributes from e-commerce data with minimal supervision, achieving a 12-point F1 improvement over baselines, expanding existing attribute types by up to 12 times, and discovering values from 39% new types.
We present a new task setting for attribute mining on e-commerce products, serving as a practical solution to extract open-world attributes without extensive human intervention. Our supervision comes from a high-quality seed attribute set bootstrapped from existing resources, and we aim to expand the attribute vocabulary of existing seed types, and also to discover any new attribute types automatically. A new dataset is created to support our setting, and our approach Amacer is proposed specifically to tackle the limited supervision. Especially, given that no direct supervision is available for those unseen new attributes, our novel formulation exploits self-supervised heuristic and unsupervised latent attributes, which attains implicit semantic signals as additional supervision by leveraging product context. Experiments suggest that our approach surpasses various baselines by 12 F1, expanding attributes of existing types significantly by up to 12 times, and discovering values from 39% new types.