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GENIA corpus—a semantically annotated corpus for bio-textmining
Abstract
Motivation: Natural language processing (NLP) methods are regarded as being useful to raise the potential of text mining from biological literature. The lack of an extensively annotated corpus of this literature, however, causes a major bottleneck for applying NLP techniques. GENIA corpus is being developed to provide reference materials to let NLP techniques work for bio-textmining. Results: GENIA corpus version 3.0 consisting of 2000 MEDLINE abstracts has been released with more than 400 000 words and almost 100 000 annotations for biological terms. Availability: GENIA corpus is freely available at http://www-tsujii.is.s.u-tokyo.ac.jp/GENIA Keywords: Text Mining, Information Extraction, Corpus, Natural Language Processing, Computational Molecular Biology *To whom correspondence should be addressed.
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Cited by
SciBERT: A Pretrained Language Model for Scientific Text
Beltagy, I., Lo, K., & Cohan, A. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1371 · Cited by 3,218
NCBI disease corpus: A resource for disease name recognition and concept normalization
Doğan, R. I., Leaman, R., & Lu, Z. (2014) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2013.12.006 · Cited by 820
A Unified MRC Framework for Named Entity Recognition
Li, X., Feng, J., Meng, Y., Han, Q., Wu, F., & Li, J. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.519 · Cited by 697
S2ORC: The Semantic Scholar Open Research Corpus
Lo, K., Wang, L. L., Neumann, M., Kinney, R., & Weld, D. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.447 · Cited by 457
A Unified Generative Framework for Various NER Subtasks
Yan, H., Gui, T., Dai, J., Guo, Q., Zhang, Z., & Qiu, X. (2021) · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · doi:10.18653/v1/2021.acl-long.451 · Cited by 299
“…been a fundamental task of Natural Language Processing (NLP), and three kinds of NER subtasks have been recognized in previous work (Sang and Meulder, 2003; Pradhan et al., 2013a; Doddington et al., 2004; Kim et al., 2003; Karimi et al., 2015), including flat NER, nested NER, and discontinuous NER.”
BackgroundMethod
Metadata
- Authors
- Kim, J.-D., Ohta, T., Tateisi, Y., & Tsujii, J.
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2003-07-03
- Volume
- 19
- Issue
- suppl_1
- Pages
- i180-i182
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- OpenAlex
- W2163107094
- Cited by
- 1,232
- References
- 0
- FWCI
- 13.08
- Type
- journal article
- Language
- en
- Open access
- closed
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- Kim, J.-D., Ohta, T., Tateisi, Y., & Tsujii, J. (2003). GENIA corpus—a semantically annotated corpus for bio-textmining. Bioinformatics. https://doi.org/10.1093/bioinformatics/btg1023
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
