# Bootstrapping Named Entity Recognition in E-Commerce with Positive Unlabeled Learning

arXiv: https://arxiv.org/abs/2005.11075 · HTML: https://mlsanity.com/paper/2005.11075/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 May 2020.

## Built on by (most cited first, 4 of 4)

- [EcomGPT: Instruction-tuning Large Language Models with Chain-of-Task Tasks for E-commerce](https://mlsanity.com/paper/2308.06966/) (2023)
- [A Unified Generative Approach to Product Attribute-Value Identification](https://mlsanity.com/paper/2306.05605/) (2023)
- [QueryNER: Segmentation of E-commerce Queries](https://mlsanity.com/paper/2405.09507/) (2024)
- [A Comparative Study of Pre-trained Encoders for Low-Resource Named Entity Recognition](https://mlsanity.com/paper/2204.04980/) (2022)

## Abstract (arXiv)

Named Entity Recognition (NER) in domains like e-commerce is an understudied problem due to the lack of annotated datasets. Recognizing novel entity types in this domain, such as products, components, and attributes, is challenging because of their linguistic complexity and the low coverage of existing knowledge resources. To address this problem, we present a bootstrapped positive-unlabeled learning algorithm that integrates domain-specific linguistic features to quickly and efficiently expand the seed dictionary. The model achieves an average F1 score of 72.02% on a novel dataset of product descriptions, an improvement of 3.63% over a baseline BiLSTM classifier, and in particular exhibits better recall (4.96% on average).