This paper focuses on a traditional relation extraction task in the context of limited annotated data and a narrow knowledge domain. We explore this task with a clinical corpus consisting of 200 breast cancer follow-up treatment letters in which 16 distinct types of relations are annotated. We experiment with an approach to extracting typed relations called window-bounded co-occurrence (WBC), which uses an adjustable context window around entity mentions of a relevant type, and compare its performance with a more typical intra-sentential co-occurrence baseline. We further introduce a new bag-of-concepts (BoC) approach to feature engineering based on the state-of-the-art word embeddings and word synonyms. We demonstrate the competitiveness of BoC by comparing with methods of higher complexity, and explore its effectiveness on this small dataset.
@article{arxiv.1904.10743,
title = {A bag-of-concepts model improves relation extraction in a narrow knowledge domain with limited data},
author = {Jiyu Chen and Karin Verspoor and Zenan Zhai},
journal= {arXiv preprint arXiv:1904.10743},
year = {2019}
}
Comments
To appear in Proceedings of the Student Research Workshop at the North American Association for Computational Linguistics (NAACL) meeting 2019