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        <title>Toponym Detection in the Bio-Medical Domain: A Hybrid Approach with Deep Learning</title>
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        <namePart type="given">Alistair</namePart>
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    <name type="personal">
        <namePart type="given">Tharindu</namePart>
        <namePart type="family">Ranasinghe</namePart>
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    <name type="personal">
        <namePart type="given">Constantin</namePart>
        <namePart type="family">Orasan</namePart>
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            <title>Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)</title>
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            <namePart type="given">Ruslan</namePart>
            <namePart type="family">Mitkov</namePart>
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        <name type="personal">
            <namePart type="given">Galia</namePart>
            <namePart type="family">Angelova</namePart>
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    <abstract>This paper compares how different machine learning classifiers can be used together with simple string matching and named entity recognition to detect locations in texts. We compare five different state-of-the-art machine learning classifiers in order to predict whether a sentence contains a location or not. Following this classification task, we use a string matching algorithm with a gazetteer to identify the exact index of a toponym within the sentence. We evaluate different approaches in terms of machine learning classifiers, text pre-processing and location extraction on the SemEval-2019 Task 12 dataset, compiled for toponym resolution in the bio-medical domain. Finally, we compare the results with our system that was previously submitted to the SemEval-2019 task evaluation.</abstract>
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    <identifier type="doi">10.26615/978-954-452-056-4_106</identifier>
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%0 Conference Proceedings
%T Toponym Detection in the Bio-Medical Domain: A Hybrid Approach with Deep Learning
%A Plum, Alistair
%A Ranasinghe, Tharindu
%A Orasan, Constantin
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)
%D 2019
%8 September
%I INCOMA Ltd.
%C Varna, Bulgaria
%F plum-etal-2019-toponym
%X This paper compares how different machine learning classifiers can be used together with simple string matching and named entity recognition to detect locations in texts. We compare five different state-of-the-art machine learning classifiers in order to predict whether a sentence contains a location or not. Following this classification task, we use a string matching algorithm with a gazetteer to identify the exact index of a toponym within the sentence. We evaluate different approaches in terms of machine learning classifiers, text pre-processing and location extraction on the SemEval-2019 Task 12 dataset, compiled for toponym resolution in the bio-medical domain. Finally, we compare the results with our system that was previously submitted to the SemEval-2019 task evaluation.
%R 10.26615/978-954-452-056-4_106
%U https://aclanthology.org/R19-1106
%U https://doi.org/10.26615/978-954-452-056-4_106
%P 912-921

Markdown (Informal)

[Toponym Detection in the Bio-Medical Domain: A Hybrid Approach with Deep Learning](https://aclanthology.org/R19-1106) (Plum et al., RANLP 2019)

• Toponym Detection in the Bio-Medical Domain: A Hybrid Approach with Deep Learning (Plum et al., RANLP 2019)

ACL

• Alistair Plum, Tharindu Ranasinghe, and Constantin Orasan. 2019. Toponym Detection in the Bio-Medical Domain: A Hybrid Approach with Deep Learning. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019), pages 912–921, Varna, Bulgaria. INCOMA Ltd..