A Multilevel NER Framework for Automatic Clinical Name Entity Recognition

Thoai Man LUU, Rob PHAN, Rachel DAVEY, Girija CHETTY

Research output: A Conference proceeding or a Chapter in BookConference contribution

1 Citation (Scopus)

Abstract

In this paper, we propose a novel multilevel NER framework, for addressing the challenges of clinical name entity recognition, based on different machine learning and text mining algorithms. The proposed framework, with multiple levels, allows models for increasingly complex NER tasks to be built. The experimental evaluation on two different publicly available datasets, corresponding to different application contexts - the CLEF 2016 challenge shared task 1A for nursing handover context, and the BIONLP/NLPBPA 2004 challenge shared task on GENIA corpus for recognizing entities in microbiology, has validated the proposed framework.
Original languageEnglish
Title of host publication2017 IEEE International Conference on Data Mining Workshops
Place of PublicationNew Orleans
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages1135-1143
Number of pages8
ISBN (Electronic)9781538638002
ISBN (Print)9781538638019
DOIs
Publication statusPublished - 18 Nov 2017
Event2017 IEEE International Conference on Data Mining Workshops (ICDMW) - New Orleans, New Orleans, United States
Duration: 18 Nov 201721 Nov 2017

Publication series

NameIEEE INTERNATIONAL CONFERENCE ON DATA MINING WORKSHOPS
ISSN (Electronic)2375-9259

Conference

Conference2017 IEEE International Conference on Data Mining Workshops (ICDMW)
Abbreviated titleICDMW 2017
CountryUnited States
CityNew Orleans
Period18/11/1721/11/17

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LUU, T. M., PHAN, R., DAVEY, R., & CHETTY, G. (2017). A Multilevel NER Framework for Automatic Clinical Name Entity Recognition. In 2017 IEEE International Conference on Data Mining Workshops (pp. 1135-1143). (IEEE INTERNATIONAL CONFERENCE ON DATA MINING WORKSHOPS). New Orleans: IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ICDMW.2017.161