Biomedical negation scope detection with conditional random fields. Agarwal, S. & Yu, H. Journal of the American Medical Informatics Association: JAMIA, 17(6):696–701, November, 2010. 00033 PMID: 20962133 PMCID: PMC3000754![Biomedical negation scope detection with conditional random fields [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex \textlessAbstractText Label="OBJECTIVE" NlmCategory="OBJECTIVE"\textgreaterNegation 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.\textless/AbstractText\textgreater \textlessAbstractText Label="DESIGN" NlmCategory="METHODS"\textgreaterConditional 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.\textless/AbstractText\textgreater \textlessAbstractText Label="MEASUREMENT" NlmCategory="METHODS"\textgreaterThe 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.\textless/AbstractText\textgreater \textlessAbstractText Label="RESULTS" NlmCategory="RESULTS"\textgreaterThe 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.\textless/AbstractText\textgreater \textlessAbstractText Label="CONCLUSIONS" NlmCategory="CONCLUSIONS"\textgreaterThis 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.\textless/AbstractText\textgreater
@article{agarwal_biomedical_2010,
title = {Biomedical negation scope detection with conditional random fields},
volume = {17},
issn = {1527-974X},
url = {http://www.ncbi.nlm.nih.gov/pubmed/20962133},
doi = {10.1136/jamia.2010.003228},
abstract = {{\textless}AbstractText Label="OBJECTIVE" NlmCategory="OBJECTIVE"{\textgreater}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.{\textless}/AbstractText{\textgreater}
{\textless}AbstractText Label="DESIGN" NlmCategory="METHODS"{\textgreater}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.{\textless}/AbstractText{\textgreater}
{\textless}AbstractText Label="MEASUREMENT" NlmCategory="METHODS"{\textgreater}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.{\textless}/AbstractText{\textgreater}
{\textless}AbstractText Label="RESULTS" NlmCategory="RESULTS"{\textgreater}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.{\textless}/AbstractText{\textgreater}
{\textless}AbstractText Label="CONCLUSIONS" NlmCategory="CONCLUSIONS"{\textgreater}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.{\textless}/AbstractText{\textgreater}},
number = {6},
urldate = {2011-03-25},
journal = {Journal of the American Medical Informatics Association: JAMIA},
author = {Agarwal, Shashank and Yu, Hong},
month = nov,
year = {2010},
note = {00033 PMID: 20962133 PMCID: PMC3000754},
keywords = {Humans, natural language processing},
pages = {696--701},
}
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However, due to the complexity of language, identifying the scope of negation in a sentence is not a trivial task.\\textless/AbstractText\\textgreater \\textlessAbstractText Label=\"DESIGN\" NlmCategory=\"METHODS\"\\textgreaterConditional 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.\\textless/AbstractText\\textgreater \\textlessAbstractText Label=\"MEASUREMENT\" NlmCategory=\"METHODS\"\\textgreaterThe 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.\\textless/AbstractText\\textgreater \\textlessAbstractText Label=\"RESULTS\" NlmCategory=\"RESULTS\"\\textgreaterThe 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.\\textless/AbstractText\\textgreater \\textlessAbstractText Label=\"CONCLUSIONS\" NlmCategory=\"CONCLUSIONS\"\\textgreaterThis 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.\\textless/AbstractText\\textgreater","number":"6","urldate":"2011-03-25","journal":"Journal of the American Medical Informatics Association: JAMIA","author":[{"propositions":[],"lastnames":["Agarwal"],"firstnames":["Shashank"],"suffixes":[]},{"propositions":[],"lastnames":["Yu"],"firstnames":["Hong"],"suffixes":[]}],"month":"November","year":"2010","note":"00033 PMID: 20962133 PMCID: PMC3000754","keywords":"Humans, natural language processing","pages":"696–701","bibtex":"@article{agarwal_biomedical_2010,\n\ttitle = {Biomedical negation scope detection with conditional random fields},\n\tvolume = {17},\n\tissn = {1527-974X},\n\turl = {http://www.ncbi.nlm.nih.gov/pubmed/20962133},\n\tdoi = {10.1136/jamia.2010.003228},\n\tabstract = {{\\textless}AbstractText Label=\"OBJECTIVE\" NlmCategory=\"OBJECTIVE\"{\\textgreater}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.{\\textless}/AbstractText{\\textgreater}\n{\\textless}AbstractText Label=\"DESIGN\" NlmCategory=\"METHODS\"{\\textgreater}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.{\\textless}/AbstractText{\\textgreater}\n{\\textless}AbstractText Label=\"MEASUREMENT\" NlmCategory=\"METHODS\"{\\textgreater}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.{\\textless}/AbstractText{\\textgreater}\n{\\textless}AbstractText Label=\"RESULTS\" NlmCategory=\"RESULTS\"{\\textgreater}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.{\\textless}/AbstractText{\\textgreater}\n{\\textless}AbstractText Label=\"CONCLUSIONS\" NlmCategory=\"CONCLUSIONS\"{\\textgreater}This approach is robust, as it can identify negation scope in both biological and clinical text. 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