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Enhancing Medical Named Entity Recognition with Features Derived from Unsupervised Methods
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
2014 (English)In: Proceedings of the Student Research Workshop at the 14th Conference of the European Chapter of the Association for Computational Linguistics, Stroudsburg, PA: Association for Computational Linguistics, 2014, 21-30 p.Conference paper, Published paper (Refereed)
Abstract [en]

A study of the usefulness of features extracted from unsupervised methods is pro- posed. The usefulness of these features will be studied on the task of performing named entity recognition within one clinical sub-domain as well as on the task of adapting a named entity recognition model to a new clinical sub-domain. Four named entity types, all very relevant for clinical information extraction, will be studied: Disorder, Finding, Pharmaceutical Drug and Body Structure. The named entity recognition will be performed using conditional random fields. As unsupervised features, a clustering of the semantic representation of words obtained from a ran- dom indexing word space will be used.

Place, publisher, year, edition, pages
Stroudsburg, PA: Association for Computational Linguistics, 2014. 21-30 p.
National Category
Information Systems
Research subject
Computer and Systems Sciences
Identifiers
URN: urn:nbn:se:su:diva-110982OAI: oai:DiVA.org:su-110982DiVA: diva2:773756
Conference
Student Research Workshop at the 14th Conference of the European Chapter of the Association for Computational Linguistics, Gothenburg, Sweden, April 26-30 2014
Available from: 2014-12-19 Created: 2014-12-19 Last updated: 2015-06-09Bibliographically approved

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