# Negation and Speculation in NLP: A Survey, Corpora, Methods, and Applications
## 4 Applications
### Biomedical Domain
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Ref.** & **Algorithm/Method** & **Phenomenon** & **Language** & **Corpus** & **F1-Score** & **PCS** \\ \hline
[66] & NegFinder & Negation & English &
\begin{tabular}{c} Surgical notes \\ and discharge \\ summaries \\ \end{tabular} & 96 & \\ \hline
[71] & NegEx & Negation & English &
\begin{tabular}{c} Discharge \\ summaries \\ \end{tabular} & 90 & \\ \hline
[78] & ConText & Negation & English & Clinical reports & 93 \\ \hline
[79] & NegMiner & Negation & English &
\begin{tabular}{c} Narrative \\ Medical \\ Documents \\ \end{tabular} & 95 \\ \hline
[10] & DEEPEN & Negation & English & Clinical notes & 81 \\ \hline
[37] & NegBio & Negation & English & \
\begin{tabular}{c} OpenI [124] \\ ChestX-ray [37] \\ \end{tabular} &
\begin{tabular}{c} 87 * \\ 94 * \\ \end{tabular} \\ \hline
[83] & SVM & Speculation & English &
\begin{tabular}{c} Papers from \\ FlyBase \\ \end{tabular} & 76 PEP 1 \\ \hline
[84] & SVM & Speculation & English & Papers from BMC & 85 PEP \\ \hline
[90] & CNN with attention & Speculation & English & CoNLL 2010 & 85 \\ \hline
[60] & contextD & Negation & Dutch & Clinical reports & 87–93 \\ \hline
[46] & CRF & Negation & Chinese & EMR & 95 \\ \hline
[95] & GAN with attention &
\begin{tabular}{c} Negation \\ Speculation \\ \end{tabular} & Arabic & BioArabic & 79 \\ \hline
[61] & NegEx & \
\begin{tabular}{c} Negation \\ Speculation \\ \end{tabular} & German & GNSC &
\begin{tabular}{c} 94 \\ 42 \\ \end{tabular} \\ \hline
[24] & BiLSTM-CRF & \
\begin{tabular}{c} Negation \\ Speculation \\ \end{tabular} & French & ESSAI-CAS &
\begin{tabular}{c} 90 \\ 86 \\ \end{tabular} \\ \hline
[13] & BiLSTM-CRF & \begin{tabular}{c} Cross-lingual \\ Negation \\ \end{tabular} & \
\begin{tabular}{c} French \\ Brazilian \\ Portuguese \\ \end{tabular} &
\begin{tabular}{c} ESSAI- \\ CASREBEC \\ \end{tabular} & 53 \\ \hline \end{tabular}  
* Precision/recall break-even point (PEP).
* Information extraction system’s performance.  
\end{table} 
Table 5: Negation and speculation detection systems for the biomedical domain. Negation- and speculation-aware models have been included in the pipeline of biomedical NLP tasks, where the biomedical data commonly include negating and speculative instances. For example, cancer diagnosis is a sensitive task requiring highly accurate systems. [PERSON] et al. assessed the classification task of identifying patients expected to be at high risk of suicide with negation detection [125]. In their study, if a negation cue was found in the syntactic tree of an input text, a rule-based algorithm detected its scope. As a result, their approach outperformed the NegEx algorithm and achieved a recall metric of 95%. Another study used a hybrid approach that combines deep learning techniques and rule-based methods to diagnose lung cancer in Spanish clinical narratives [12]. First, the BiLST-CRF model extracts the named entities; next, handcrafted rules detect the negating and speculative scope, and finally, it reports the lung cancer diagnosis. The proposed negation and speculation rule-based approach improved the task of relating cancer diagnosis to a correct date by 18% and achieved an 89 F1 score. These evaluation results prove that clinical notes are sensitive to negation and speculation phenomena.