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Classifying semantic relations in bioscience texts
Open accessAbstract
A crucial step toward the goal of automatic extraction of propositional information from natural language text is the identification of semantic relations between constituents in sentences. We examine the problem of distinguishing among seven relation types that can occur between the entities "treatment" and "disease" in bioscience text, and the problem of identifying such entities. We compare five generative graphical models and a neural network, using lexical, syntactic, and semantic features, finding that the latter help achieve high classification accuracy.
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Cited by
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Automatic Summarization of Results from Clinical Trials
Summerscales, R. L., Argamon, S., Bai, S., Hupert, J., & Schwartz, A. (2011) · 2011 IEEE International Conference on Bioinformatics and Biomedicine · doi:10.1109/bibm.2011.72 · Cited by 49
Improving the robustness and accuracy of biomedical language models through adversarial training
Moradi, M., & Samwald, M. (2022) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2022.104114 · Cited by 18
Evaluating automated entity extraction with respect to drug and non-drug treatment strategies.
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Metadata
- Authors
- Rosario, B., & Hearst, M. A.
- Venue
- Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics - ACL '04
- Published
- 2004
- Pages
- 430-es
- OpenAlex
- W2110279753
- Cited by
- 257
- References
- 0
- FWCI
- 29.47
- Type
- conference paper
- Language
- en
- Open access
- bronze
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- Rosario, B., & Hearst, M. A. (2004). Classifying semantic relations in bioscience texts. Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics - ACL '04. https://doi.org/10.3115/1218955.1219010
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