Towards Open-World Product Attribute Mining: A Lightly-Supervised
  Approach
Liyan Xu Chenwei Zhang Xian Li Jingbo Shang Jinho D. Choi
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.