# Towards Open-World Product Attribute Mining: A Lightly-Supervised Approach

arXiv: https://arxiv.org/abs/2305.18350 · HTML: https://mlsanity.com/paper/2305.18350/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 27 references that are in the map.
- First posted on arXiv 26 May 2023.

## Builds on (most cited first, 15 of 15)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [Topic Modeling in Embedding Spaces](https://mlsanity.com/paper/1907.04907/) (2019)
- [Autoencoding Variational Inference For Topic Models](https://mlsanity.com/paper/1703.01488/) (2017)
- [Perturbed Masking: Parameter-free Probing for Analyzing and Interpreting BERT](https://mlsanity.com/paper/2004.14786/) (2020)
- [Discovering Discrete Latent Topics with Neural Variational Inference](https://mlsanity.com/paper/1706.00359/) (2017)
- [Automated Phrase Mining from Massive Text Corpora](https://mlsanity.com/paper/1702.04457/) (2017)
- [OpenTag: Open Attribute Value Extraction from Product Profiles [Deep Learning, Active Learning, Named Entity Recognition]](https://mlsanity.com/paper/1806.01264/) (2018)
- [MAVE: A Product Dataset for Multi-source Attribute Value Extraction](https://mlsanity.com/paper/2112.08663/) (2021)
- [OA-Mine: Open-World Attribute Mining for E-Commerce Products with Weak Supervision](https://mlsanity.com/paper/2204.13874/) (2022)
- [A Relation-Oriented Clustering Method for Open Relation Extraction](https://mlsanity.com/paper/2109.07205/) (2021)
- [Unsupervised Open Relation Extraction](https://mlsanity.com/paper/1801.07174/) (2018)

## Built on by (most cited first, 6 of 6)

- [EIVEN: Efficient Implicit Attribute Value Extraction using Multimodal LLM](https://mlsanity.com/paper/2404.08886/) (2024)
- [Fine-Grained Modeling of Narrative Context: A Coherence Perspective via Retrospective Questions](https://mlsanity.com/paper/2402.13551/) (2024)
- [TACLR: A Scalable and Efficient Retrieval-based Method for Industrial Product Attribute Value Identification](https://mlsanity.com/paper/2501.03835/) (2025)
- [Visual Zero-Shot E-Commerce Product Attribute Value Extraction](https://mlsanity.com/paper/2502.15979/) (2025)
- [GSID: Generative Semantic Indexing for E-Commerce Product Understanding](https://mlsanity.com/paper/2509.23860/) (2025)
- [Scaling E-Commerce Attribute Extraction with Parallel Decoding](https://mlsanity.com/paper/2609.09716/) (2026)

## Abstract (arXiv)

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.