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        <title>Named Entity Recognition in the Medical Domain with Constrained CRF Models</title>
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        <namePart type="given">Charles</namePart>
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    <name type="personal">
        <namePart type="given">Léa</namePart>
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        <name type="personal">
            <namePart type="given">Mirella</namePart>
            <namePart type="family">Lapata</namePart>
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        <name type="personal">
            <namePart type="given">Alexander</namePart>
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    <abstract>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.</abstract>
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%0 Conference Proceedings
%T Named Entity Recognition in the Medical Domain with Constrained CRF Models
%A Jochim, Charles
%A Deleris, Léa
%Y Lapata, Mirella
%Y Blunsom, Phil
%Y Koller, Alexander
%S Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers
%D 2017
%8 April
%I Association for Computational Linguistics
%C Valencia, Spain
%F jochim-deleris-2017-named
%X 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.
%U https://aclanthology.org/E17-1079
%P 839-849

Markdown (Informal)

[Named Entity Recognition in the Medical Domain with Constrained CRF Models](https://aclanthology.org/E17-1079) (Jochim & Deleris, EACL 2017)

• Named Entity Recognition in the Medical Domain with Constrained CRF Models (Jochim & Deleris, EACL 2017)

ACL

• Charles Jochim and Léa Deleris. 2017. Named Entity Recognition in the Medical Domain with Constrained CRF Models. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, pages 839–849, Valencia, Spain. Association for Computational Linguistics.