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

Hanchu Zhang, Leonhard Hennig, Christoph Alt, Changjian Hu, Yao Meng, Chao Wang


Abstract
In this work, we introduce a bootstrapped, iterative NER model that integrates a PU learning algorithm for recognizing named entities in a low-resource setting. Our approach combines dictionary-based labeling with syntactically-informed label expansion to efficiently enrich the seed dictionaries. Experimental results on a dataset of manually annotated e-commerce product descriptions demonstrate the effectiveness of the proposed framework.
Anthology ID:
2020.ecnlp-1.1
Volume:
Proceedings of The 3rd Workshop on e-Commerce and NLP
Month:
July
Year:
2020
Address:
Seattle, WA, USA
Venues:
ACL | ECNLP | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1–6
Language:
URL:
https://aclanthology.org/2020.ecnlp-1.1
DOI:
10.18653/v1/2020.ecnlp-1.1
Bibkey:
Cite (ACL):
Hanchu Zhang, Leonhard Hennig, Christoph Alt, Changjian Hu, Yao Meng, and Chao Wang. 2020. Bootstrapping Named Entity Recognition in E-Commerce with Positive Unlabeled Learning. In Proceedings of The 3rd Workshop on e-Commerce and NLP, pages 1–6, Seattle, WA, USA. Association for Computational Linguistics.
Cite (Informal):
Bootstrapping Named Entity Recognition in E-Commerce with Positive Unlabeled Learning (Zhang et al., ECNLP 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.ecnlp-1.1.pdf
Video:
 http://slideslive.com/38931239