文章基本信息

标题：Named Entity Recognition in the Medical Domain with ConstrainedCRFModels
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作者：Charles Jochim ; Léa Deleris
期刊名称：Conference on European Chapter of the Association for Computational Linguistics (EACL)
出版年度：2017
卷号：2017
页码：839-849
语种：English
出版社：ACL Anthology
摘要：This paper investigates how to improve performance on information extraction tasks by constraining and sequencing CRF-based approaches. We consider two different relation extraction tasks, both from the medical literature: dependence relations and probability statements. We explore whether adding constraints can lead to an improvement over standard CRF decoding. Results on our relation extraction tasks are promising, showing significant increases in performance from both (i) adding constraints to post-process the output of a baseline CRF, which captures “domain knowledge”, and (ii) further allowing flexibility in the application of those constraints by leveraging a binary classifier as a pre-processing step.