Structured representations of entity names are useful for many entity-related tasks such as entity normalization and variant generation. Learning the implicit structured representations of entity names without context and external knowledge is particularly challenging. In this paper, we present a novel learning framework that combines active learning and weak supervision to solve this problem. Our experimental evaluation show that this framework enables the learning of high-quality models from merely a dozen or so labeled examples.
@article{arxiv.2011.00105,
title = {Learning Structured Representations of Entity Names using Active Learning and Weak Supervision},
author = {Kun Qian and Poornima Chozhiyath Raman and Yunyao Li and Lucian Popa},
journal= {arXiv preprint arXiv:2011.00105},
year = {2020}
}