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Toponym detection in the bio-medical domain: A hybrid approach with deep learning
Plum, Alistair Ranasinghe, Tharindu Orăsan, Constantin
Plum, Alistair
Ranasinghe, Tharindu
Orăsan, Constantin
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2019-09-02
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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.
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Plum, A., Ranasinghe, T. and Orasan, C. (2019) Toponym detection in the bio-medical domain: A hybrid approach with deep learning. RANLP 2017, 2nd-4th September, Varna, Bulgaria.
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Conference contribution
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en
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1313-8502
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9789544520557