Medical Informatics, University of Wisconsin-Milwaukee, Milwaukee, Wisconsin, USA.
J Am Med Inform Assoc. 2010 Nov-Dec;17(6):696-701. doi: 10.1136/jamia.2010.003228.
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.
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.
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.
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.
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.
否定是一种语言现象，用于标记实体或事件的不存在。否定事件在生物文献和临床记录中经常被报道。文本挖掘应用程序受益于否定及其范围的检测。然而，由于语言的复杂性，确定句子中的否定范围并不是一项简单的任务。
条件随机场（CRF）是一种监督机器学习算法，用于训练模型来检测生物文献和临床记录中的否定提示短语及其范围。这些模型是在公开的 BioScope 语料库上进行训练的。
通过计算召回率、精度和 F1 分数来评估 CRF 模型识别否定提示短语及其范围的性能。将这些模型与四个竞争基线系统进行了比较。
基于 CRF 的最佳模型在识别否定提示短语及其范围方面的性能明显优于所有基线系统和 NegEx，在识别临床记录中的否定提示短语及其范围方面的 F1 得分为 98%和 95%，在识别生物文献中的否定提示短语及其范围方面的 F1 得分为 97%和 85%。
该方法具有稳健性，因为它可以识别生物和临床文本中的否定范围。为了使文本挖掘应用程序受益，该系统以 Java API 的形式和在线应用程序（http://negscope.askhermes.org）的形式提供。
