PharmacoNER Tagger: a deep learning-based tool for automatically finding chemicals and drugs in Spanish medical texts
Automatically detecting mentions of pharmaceutical drugs and chemical substances is key for the subsequent extraction of relations of chemicals with other biomedical entities such as genes, proteins, diseases, adverse reactions or symptoms. The identification of drug mentions is also a prior step fo...
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| Format: | Article |
| Language: | English |
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BioMed Central
2019-06-01
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| Series: | Genomics & Informatics |
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| Online Access: | http://genominfo.org/upload/pdf/gi-2019-17-2-e15.pdf |
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| author | Jordi Armengol-Estapé Felipe Soares Montserrat Marimon Martin Krallinger |
| author_facet | Jordi Armengol-Estapé Felipe Soares Montserrat Marimon Martin Krallinger |
| author_sort | Jordi Armengol-Estapé |
| collection | DOAJ |
| description | Automatically detecting mentions of pharmaceutical drugs and chemical substances is key for the subsequent extraction of relations of chemicals with other biomedical entities such as genes, proteins, diseases, adverse reactions or symptoms. The identification of drug mentions is also a prior step for complex event types such as drug dosage recognition, duration of medical treatments or drug repurposing. Formally, this task is known as named entity recognition (NER), meaning automatically identifying mentions of predefined entities of interest in running text. In the domain of medical texts, for chemical entity recognition (CER), techniques based on hand-crafted rules and graph-based models can provide adequate performance. In the recent years, the field of natural language processing has mainly pivoted to deep learning and state-of-the-art results for most tasks involving natural language are usually obtained with artificial neural networks. Competitive resources for drug name recognition in English medical texts are already available and heavily used, while for other languages such as Spanish these tools, although clearly needed were missing. In this work, we adapt an existing neural NER system, NeuroNER, to the particular domain of Spanish clinical case texts, and extend the neural network to be able to take into account additional features apart from the plain text. NeuroNER can be considered a competitive baseline system for Spanish drug and CER promoted by the Spanish national plan for the advancement of language technologies (Plan TL). |
| format | Article |
| id | doaj-art-232b7f00e0dc4286a018f3dd2a16d0a6 |
| institution | Kabale University |
| issn | 2234-0742 |
| language | English |
| publishDate | 2019-06-01 |
| publisher | BioMed Central |
| record_format | Article |
| series | Genomics & Informatics |
| spelling | doaj-art-232b7f00e0dc4286a018f3dd2a16d0a62025-02-02T13:28:54ZengBioMed CentralGenomics & Informatics2234-07422019-06-0117210.5808/GI.2019.17.2.e15557PharmacoNER Tagger: a deep learning-based tool for automatically finding chemicals and drugs in Spanish medical textsJordi Armengol-Estapé0Felipe Soares1Montserrat Marimon2Martin Krallinger3 Universitat Politècnica de Catalunya (UPC), 08034 Barcelona, Spain Barcelona Supercomputing Center (BSC), 08034 Barcelona, Spain Barcelona Supercomputing Center (BSC), 08034 Barcelona, Spain Barcelona Supercomputing Center (BSC), 08034 Barcelona, SpainAutomatically detecting mentions of pharmaceutical drugs and chemical substances is key for the subsequent extraction of relations of chemicals with other biomedical entities such as genes, proteins, diseases, adverse reactions or symptoms. The identification of drug mentions is also a prior step for complex event types such as drug dosage recognition, duration of medical treatments or drug repurposing. Formally, this task is known as named entity recognition (NER), meaning automatically identifying mentions of predefined entities of interest in running text. In the domain of medical texts, for chemical entity recognition (CER), techniques based on hand-crafted rules and graph-based models can provide adequate performance. In the recent years, the field of natural language processing has mainly pivoted to deep learning and state-of-the-art results for most tasks involving natural language are usually obtained with artificial neural networks. Competitive resources for drug name recognition in English medical texts are already available and heavily used, while for other languages such as Spanish these tools, although clearly needed were missing. In this work, we adapt an existing neural NER system, NeuroNER, to the particular domain of Spanish clinical case texts, and extend the neural network to be able to take into account additional features apart from the plain text. NeuroNER can be considered a competitive baseline system for Spanish drug and CER promoted by the Spanish national plan for the advancement of language technologies (Plan TL).http://genominfo.org/upload/pdf/gi-2019-17-2-e15.pdfmachine learningnatural language processingneural networks (computer) |
| spellingShingle | Jordi Armengol-Estapé Felipe Soares Montserrat Marimon Martin Krallinger PharmacoNER Tagger: a deep learning-based tool for automatically finding chemicals and drugs in Spanish medical texts Genomics & Informatics machine learning natural language processing neural networks (computer) |
| title | PharmacoNER Tagger: a deep learning-based tool for automatically finding chemicals and drugs in Spanish medical texts |
| title_full | PharmacoNER Tagger: a deep learning-based tool for automatically finding chemicals and drugs in Spanish medical texts |
| title_fullStr | PharmacoNER Tagger: a deep learning-based tool for automatically finding chemicals and drugs in Spanish medical texts |
| title_full_unstemmed | PharmacoNER Tagger: a deep learning-based tool for automatically finding chemicals and drugs in Spanish medical texts |
| title_short | PharmacoNER Tagger: a deep learning-based tool for automatically finding chemicals and drugs in Spanish medical texts |
| title_sort | pharmaconer tagger a deep learning based tool for automatically finding chemicals and drugs in spanish medical texts |
| topic | machine learning natural language processing neural networks (computer) |
| url | http://genominfo.org/upload/pdf/gi-2019-17-2-e15.pdf |
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