Biomedical negation scope detection with conditional random fields
- 1Medical Informatics, University of Wisconsin-Milwaukee, Milwaukee, Wisconsin, USA
- 2Department of Health Sciences, University of Wisconsin-Milwaukee, Milwaukee, Wisconsin, USA
- 3Department of Computer Science, University of Wisconsin-Milwaukee, Milwaukee, Wisconsin, USA
- Correspondence to Dr Hong Yu, 2400 E Hartford Ave, Room 939, Milwaukee WI 53211, USA;
- Received 15 January 2010
- Accepted 31 August 2010
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.
Funding This work received support from the National Library of Medicine, grant number 5R01LM009836 to HY and 5R01LM010125 to Isaac Kohane.
Provenance and peer review Not commissioned; externally peer reviewed.