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://www.aclweb.org/anthology/2020.ecnlp-1.1
DOI:
10.18653/v1/2020.ecnlp-1.1
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.ecnlp-1.1.pdf
Video:
 http://slideslive.com/38931239