# Negation and Speculation in NLP: A Survey, Corpora, Methods, and Applications
## 4 Applications
### Others  
The SEM 2012 Shared Task* was announced at the first joint conference on lexical and computational semantics to motivate the research community to solve the scope and focus of negation [58]. This competition relied on the corpus of journal stories, PropBank FOC [51], and the corpus of short stories [PERSON] [41]. In 2012, the best performing system was the SVM-based UiO1 system. Later, other research groups resolved this task by other approaches. As listed in Table 7, NegBERT obtained the highest F1 score, but it did not mention the PCS metric. For the other languages, Arabic and Chinese, the RNN-based models showed good performance.  
Further NLP projects have studied the effect of negation and speculation, as in [PERSON] et al. [106], which looked at tackling phenomena of negation in MT systems. Negation-related translation issues include the omission and reversal of negation that contradicts the translated sentence, the incorrect scope of negation that negates the wrong token, and the mistranslation of the negation object. These issues vary in severity, but each may completely change the meaning of the translated sentence. The authors targeted the translation tasks in WMT18 and WMT19 competitions that translate news both from and to English and other languages. Using the BiLSTM-CRF ([[https://github.com/mosharafhossain/negation-mt](https://github.com/mosharafhossain/negation-mt)]([https://github.com/mosharafhossain/negation-mt](https://github.com/mosharafhossain/negation-mt)) (accessed on 28 March 2022)) model based on the SEM 2012* corpus, an F1 score of 92 was achieved when detecting the negation cue in English. The evaluation results showed that the presence of negation reduces the system's performance, in some cases by 60%.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Ref.**} & \multirow{2}{*}{**Algorithm/Method**} & \multicolumn{2}{c}{**Negation**} & \multicolumn{2}{c}{**Speculation**} \\ \cline{3-6}  & & **F1-Score** & **PCS** & **F1-Score** & **PCS** \\ \hline
[72] & SVM-CRF-TiMBL & 80 & 60 & & \\
[73] & SVM-CRF-TiMBL & & & 59 & 31 \\
[75] & SVM & & 71 & \\
[44] & CRF & 75 & 53 & & \\
[86] & SVM Tree Kernel & 84 & 53 & 94 & 71 \\
[87] & CNN & 89 & 74 & 91 & 74 \\
[92] & Semi-CRF & 90 & 78 & & \\
[74] & NegBERT & 93 & & & \\
[11] & BiLSTM & & 90 & & 86 \\
[96] & RNN-CRF & 93 & 92 & 91 & 88 \\
[39] & BiLSTM \& CNN & 90 & 75 & & \\
[71] & NegEx & 82 & & & \\
[37] & NegBio & 95 & & & \\
[102] & ScopeFinder & 75 & & 76 & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Detection of negation and speculation scope systems for the BioScope corpus.  
In recent years, news articles have become rapidly shared and consumed; thus, there is a need for systems to extract valuable and accurate information. In one work [126], uncertainty detection in the newswire domain was addressed as a binary classification task. The authors proposed a hybrid machine learning approach with semantic, syntactic features to classify an event as certain or uncertain. For the uncertainty identification task, this approach achieved a 71 F1-score.