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--- |
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language: |
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- en |
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bigbio_language: |
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- English |
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license: unknown |
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multilinguality: monolingual |
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bigbio_license_shortname: UNKNOWN |
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pretty_name: EU-ADR |
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homepage: https://www.sciencedirect.com/science/article/pii/S1532046412000573 |
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bigbio_pubmed: True |
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bigbio_public: True |
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bigbio_tasks: |
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- NAMED_ENTITY_RECOGNITION |
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- RELATION_EXTRACTION |
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--- |
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# Dataset Card for EU-ADR |
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## Dataset Description |
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- **Homepage:** https://www.sciencedirect.com/science/article/pii/S1532046412000573 |
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- **Pubmed:** True |
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- **Public:** True |
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- **Tasks:** NER,RE |
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Corpora with specific entities and relationships annotated are essential to train and evaluate text-mining systems that are developed to extract specific structured information from a large corpus. In this paper we describe an approach where a named-entity recognition system produces a first annotation and annotators revise this annotation using a web-based interface. The agreement figures achieved show that the inter-annotator agreement is much better than the agreement with the system provided annotations. The corpus has been annotated for drugs, disorders, genes and their inter-relationships. For each of the drug-disorder, drug-target, and target-disorder relations three experts have annotated a set of 100 abstracts. These annotated relationships will be used to train and evaluate text-mining software to capture these relationships in texts. |
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## Citation Information |
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``` |
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@article{VANMULLIGEN2012879, |
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title = {The EU-ADR corpus: Annotated drugs, diseases, targets, and their relationships}, |
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journal = {Journal of Biomedical Informatics}, |
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volume = {45}, |
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number = {5}, |
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pages = {879-884}, |
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year = {2012}, |
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note = {Text Mining and Natural Language Processing in Pharmacogenomics}, |
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issn = {1532-0464}, |
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doi = {https://doi.org/10.1016/j.jbi.2012.04.004}, |
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url = {https://www.sciencedirect.com/science/article/pii/S1532046412000573}, |
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author = {Erik M. {van Mulligen} and Annie Fourrier-Reglat and David Gurwitz and Mariam Molokhia and Ainhoa Nieto and Gianluca Trifiro and Jan A. Kors and Laura I. Furlong}, |
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keywords = {Text mining, Corpus development, Machine learning, Adverse drug reactions}, |
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abstract = {Corpora with specific entities and relationships annotated are essential to train and evaluate text-mining systems that are developed to extract specific structured information from a large corpus. In this paper we describe an approach where a named-entity recognition system produces a first annotation and annotators revise this annotation using a web-based interface. The agreement figures achieved show that the inter-annotator agreement is much better than the agreement with the system provided annotations. The corpus has been annotated for drugs, disorders, genes and their inter-relationships. For each of the drug–disorder, drug–target, and target–disorder relations three experts have annotated a set of 100 abstracts. These annotated relationships will be used to train and evaluate text-mining software to capture these relationships in texts.} |
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} |
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``` |
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