# Negation and Speculation in NLP: A Survey, Corpora, Methods, and Applications
## 5 Discussion  
Corpora annotated with negation and speculation vary in language, domain, size, and annotated span level (cue, scope, event, and focus). Cue is the most important element, as it influences the other annotated elements, and without it, negation or speculation cannot be handled. Negation has four tasks: cue detection, scope identification, event extraction, and focus identification. Almost all corpora annotated with negation include an annotation for a negation cue and its scope; however, some corpora do not include any negation cue, as in English [21; 51], Dutch [60], and Hungarian [29]. Event element is annotated mainly for the biomedical domain, and recently has been included in further domains, such as reviews [26] and news [35] in the Spanish language. Corpora annotated with focus are scarce, because annotating a corpus appropriately involves great attention. However, it is necessary to review the annotation guidelines to eliminate inconsistent annotations, even in the same language and in the same domain. Corpora annotated with negation were mainly built to develop negation-aware systems. At first, the research community focused on the treatment of negation in clinical texts using the rule-based algorithms. The NegEx algorithm was widely used to detect negation especially in clinical texts, since the nature of any text in this domain is syntactically restricted. The BioScope corpus is an example [14]. Despite its simplistic form, the algorithm achieved remarkable results on a variety of clinical notes. Dependency-based parser tools did not achieve the same desired performance compared to their complexities. Nonetheless, NegBio was the first rule-based algorithm to handle the speculative instances in the biomedical domain. Most of these algorithms were built to handle negation phenomena for clinical reports; however, other research studies have handcrafted the rules to the task of ASA [3; 117]. Despite NegEx being language-dependent and seemingly a relatively basic tool with which to identify negating events/text, it is considered the better rule-based algorithm compared to more complex ones [10; 37]. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Ref.** & **Algorithm/Method** & **Phenomenon** & **Language** & **Corpus** & **F1-Score** & **PCS** \\ \hline [76] & MRS Crawler & Negation & English & Conan Doyle & & 75 \\ \hline [85] & SVM & Negation & English & SEM 2012\({}^{\star}\) & 78 & \\ \hline [89] & BiLSTM & Negation & English & SEM 2012\({}^{\star}\) & 89 & \\ \hline [91] & SVM+CRF & Negation & English & SEM 2012\({}^{\star}\) & 71 & \\ \hline [97] & STRNN & Negation & English & SEM 2012\({}^{\star}\) & 89 & \\ \hline [74] & NegBERT & Negation & English & SEM 2012\({}^{\star}\) & 92 & \\ \hline [92] & Semi-CRF & Negation & English & SEM 2012\({}^{\star}\) & 88 & 82 \\ \hline [96] & RNN-CRF & Negation & Chinese & ChNeSp & 90 & 72 \\ \hline [11] & BiLSTM & Negation & English-Chinese & NegPar & & 79 \\ \hline [94] & BiLSTM &
\begin{tabular}{c} Cross-Lingual \\ Negation \\ \end{tabular} & English-Chinese & NegPar & 72 & 24 \\ \hline [36] & BiLSTM & Negation & Arabic & ArNe & 89 & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Negation and speculation detection systems for other domains. In the biomedical domain, rule-based algorithms succeeded in identifying the scope of negation, yet they were not used in other domains, such as review data or general text data. This is for several reasons: their text may include spelling mistakes and implicit negation or speculation, plus it is freestyle writing that does not follow grammatical rules. As a result, in various domains supervised-based models were proposed to address the problem of negation. These models typically either use external resources to extract complex syntax and grammar features or are based on neural architectures, such as LSTM and CNNs, to extract the semantic representations and capture the hidden features automatically.