The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
BioInfer: a corpus for information extraction in the biomedical domain
Open accessAbstract
BACKGROUND: Lately, there has been a great interest in the application of information extraction methods to the biomedical domain, in particular, to the extraction of relationships of genes, proteins, and RNA from scientific publications. The development and evaluation of such methods requires annotated domain corpora. RESULTS: We present BioInfer (Bio Information Extraction Resource), a new public resource providing an annotated corpus of biomedical English. We describe an annotation scheme capturing named entities and their relationships along with a dependency analysis of sentence syntax. We further present ontologies defining the types of entities and relationships annotated in the corpus. Currently, the corpus contains 1100 sentences from abstracts of biomedical research articles annotated for relationships, named entities, as well as syntactic dependencies. Supporting software is provided with the corpus. The corpus is unique in the domain in combining these annotation types for a single set of sentences, and in the level of detail of the relationship annotation. CONCLUSION: We introduce a corpus targeted at protein, gene, and RNA relationships which serves as a resource for the development of information extraction systems and their components such as parsers and domain analyzers. The corpus will be maintained and further developed with a current version being available at http://www.it.utu.fi/BioInfer.
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Cites
Gene Ontology: tool for the unification of biology
Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., Davis, A. P., Dolinski, K., Dwight, S. S., Eppig, J. T., et al. (2000) · Nature Genetics · doi:10.1038/75556 · Cited by 43,206
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Deep learning with word embeddings improves biomedical named entity recognition
Habibi, M., Weber, L., Neves, M., Wiegandt, D. L., & Leser, U. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx228 · Cited by 542
BioWordVec, improving biomedical word embeddings with subword information and MeSH
Zhang, Y., Chen, Q., Yang, Z., Lin, H., & Lu, Z. (2019) · Scientific Data · doi:10.1038/s41597-019-0055-0 · Cited by 455
Recent advances in biomedical literature mining
Zhao, S., Su, C., Lu, Z., & Wang, F. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa057 · Cited by 134
PKDE4J: Entity and relation extraction for public knowledge discovery
Song, M., Kim, W. C., Lee, D., Heo, G. E., & Kang, K. Y. (2015) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2015.08.008 · Cited by 120
Metadata
- Authors
- Pyysalo, S., Ginter, F., Heimonen, J., Björne, J., Boberg, J., Järvinen, J., & Salakoski, T.
- Venue
- BMC Bioinformatics
- Published
- 2007-02-09
- Volume
- 8
- Issue
- 1
- Pages
- 50-50
- ISSN
- 1471-2105 (ISSN-L)
- OpenAlex
- W1850865022
- Cited by
- 485
- References
- 1
- FWCI
- 13.05
- Type
- journal article
- Language
- en
- Open access
- gold
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- Pyysalo, S., Ginter, F., Heimonen, J., Björne, J., Boberg, J., Järvinen, J., & Salakoski, T. (2007). BioInfer: a corpus for information extraction in the biomedical domain. BMC Bioinformatics. https://doi.org/10.1186/1471-2105-8-50
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