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Cited by 418

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EDGAR: Extraction of Drugs, Genes And Relations from the Biomedical Literature

Open access
Rindflesch, Thomas C., Tanabe, Lorraine, Weinstein, John N., Hunter, Lawrence·1999·Biocomputing 2000

Abstract

EDGAR (Extraction of Drugs, Genes and Relations) is a natural language processing system that extracts information about drugs and genes relevant to cancer from the biomedical literature. This automatically extracted information has remarkable potential to facilitate computational analysis in the molecular biology of cancer, and the technology is straightforwardly generalizable to many areas of biomedicine. This paper reports on the mechanisms for automatically generating such assertions and on a simple application, conceptual clustering of documents. The system uses a stochastic part of speech tagger, generates an underspecified syntactic parse and then uses semantic and pragmatic information to construct its assertions. The system builds on two important existing resources: the MEDLINE database of biomedical citations and abstracts and the Unified Medical Language System, which provides syntactic and semantic information about the terms found in biomedical abstracts.

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Metadata

Authors
Rindflesch, T. C., Tanabe, L., Weinstein, J. N., & Hunter, L.
Venue
Biocomputing 2000
Published
1999-12
Pages
517-528
OpenAlex
W2023736097
Cited by
418
References
0
FWCI
51.48
Type
conference paper
Language
en
Open access
green

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CoChat. (2026). Citation check: EDGAR: Extraction of Drugs, Genes And Relations from the Biomedical Literature. https://app.cochat.ai/papers/10.1142/9789814447331_0049
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Rindflesch, T. C., Tanabe, L., Weinstein, J. N., & Hunter, L. (1999). EDGAR: Extraction of Drugs, Genes And Relations from the Biomedical Literature. Biocomputing 2000. https://doi.org/10.1142/9789814447331_0049

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

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.