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Database of negated biomedical sentences in literature consisting of more than 32 million negated sentences. Negated sentences were detected using the algorithm described in - Shashank Agarwal, Hong Yu Biomedical negation scope detection with Conditional Random Fields Journal of the American Medical Informatics Association (JAMIA), 2010; 17:696-701. After entering your query in the search box (for example MeCP2 autism), the search results with the negated sentence and the sentences preceding and following the negated sentences are displayed. A link to the source of the sentence is also provided, which links to the article from which the negated sentence was extracted. BioNOT is no longer updated. Documented 2013.

URL: http://snake.ims.uwm.edu/bionot/index.php?searchterm=mecp2+autism&submit=Search

Resource ID: nlx_143912     Resource Type: Resource     Version: Latest Version


biomedical, negated sentence, bibliographic



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NIF Data Federation



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Original Submitter


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Submitted On

12:00am January 5, 2012

Originated From


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  • Description was changed
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Version 2

Created 2 months ago by Christie Wang

Version 1

Created 4 years ago by Anonymous

Biomedical negation scope detection with conditional random fields.

  • Agarwal S
  • J Am Med Inform Assoc
  • 2010 21

OBJECTIVE: Negation is a linguistic phenomenon that marks the absence of an entity or event. Negated events are frequently reported in both biological literature and clinical notes. Text mining applications benefit from the detection of negation and its scope. However, due to the complexity of language, identifying the scope of negation in a sentence is not a trivial task. DESIGN: Conditional random fields (CRF), a supervised machine-learning algorithm, were used to train models to detect negation cue phrases and their scope in both biological literature and clinical notes. The models were trained on the publicly available BioScope corpus. MEASUREMENT: The performance of the CRF models was evaluated on identifying the negation cue phrases and their scope by calculating recall, precision and F1-score. The models were compared with four competitive baseline systems. RESULTS: The best CRF-based model performed statistically better than all baseline systems and NegEx, achieving an F1-score of 98% and 95% on detecting negation cue phrases and their scope in clinical notes, and an F1-score of 97% and 85% on detecting negation cue phrases and their scope in biological literature. CONCLUSIONS: This approach is robust, as it can identify negation scope in both biological and clinical text. To benefit text mining applications, the system is publicly available as a Java API and as an online application at http://negscope.askhermes.org.