GGPONC: A Corpus of German Medical Text with Rich Metadata Based on Clinical Practice Guidelines
The lack of publicly accessible text corpora is a major obstacle for progress in natural language processing. For medical applications, unfortunately, all language communities other than English are low-resourced. In this work, we present GGPONC (German Guideline Program in Oncology NLP Corpus), a f...
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| creator | Borchert, Florian Lohr, Christina Modersohn, Luise Langer, Thomas Follmann, Markus Sachs, Jan Philipp Hahn, Udo Schapranow, Matthieu-P |
| description | The lack of publicly accessible text corpora is a major obstacle for progress
in natural language processing. For medical applications, unfortunately, all
language communities other than English are low-resourced. In this work, we
present GGPONC (German Guideline Program in Oncology NLP Corpus), a freely
distributable German language corpus based on clinical practice guidelines for
oncology. This corpus is one of the largest ever built from German medical
documents. Unlike clinical documents, clinical guidelines do not contain any
patient-related information and can therefore be used without data protection
restrictions. Moreover, GGPONC is the first corpus for the German language
covering diverse conditions in a large medical subfield and provides a variety
of metadata, such as literature references and evidence levels. By applying and
evaluating existing medical information extraction pipelines for German text,
we are able to draw comparisons for the use of medical language to other
corpora, medical and non-medical ones. |
| doi_str_mv | 10.48550/arxiv.2007.06400 |
| format | Article |
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in natural language processing. For medical applications, unfortunately, all
language communities other than English are low-resourced. In this work, we
present GGPONC (German Guideline Program in Oncology NLP Corpus), a freely
distributable German language corpus based on clinical practice guidelines for
oncology. This corpus is one of the largest ever built from German medical
documents. Unlike clinical documents, clinical guidelines do not contain any
patient-related information and can therefore be used without data protection
restrictions. Moreover, GGPONC is the first corpus for the German language
covering diverse conditions in a large medical subfield and provides a variety
of metadata, such as literature references and evidence levels. By applying and
evaluating existing medical information extraction pipelines for German text,
we are able to draw comparisons for the use of medical language to other
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in natural language processing. For medical applications, unfortunately, all
language communities other than English are low-resourced. In this work, we
present GGPONC (German Guideline Program in Oncology NLP Corpus), a freely
distributable German language corpus based on clinical practice guidelines for
oncology. This corpus is one of the largest ever built from German medical
documents. Unlike clinical documents, clinical guidelines do not contain any
patient-related information and can therefore be used without data protection
restrictions. Moreover, GGPONC is the first corpus for the German language
covering diverse conditions in a large medical subfield and provides a variety
of metadata, such as literature references and evidence levels. By applying and
evaluating existing medical information extraction pipelines for German text,
we are able to draw comparisons for the use of medical language to other
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in natural language processing. For medical applications, unfortunately, all
language communities other than English are low-resourced. In this work, we
present GGPONC (German Guideline Program in Oncology NLP Corpus), a freely
distributable German language corpus based on clinical practice guidelines for
oncology. This corpus is one of the largest ever built from German medical
documents. Unlike clinical documents, clinical guidelines do not contain any
patient-related information and can therefore be used without data protection
restrictions. Moreover, GGPONC is the first corpus for the German language
covering diverse conditions in a large medical subfield and provides a variety
of metadata, such as literature references and evidence levels. By applying and
evaluating existing medical information extraction pipelines for German text,
we are able to draw comparisons for the use of medical language to other
corpora, medical and non-medical ones.</abstract><doi>10.48550/arxiv.2007.06400</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Computer Science - Computation and Language |
| title | GGPONC: A Corpus of German Medical Text with Rich Metadata Based on Clinical Practice Guidelines |
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