# Negation and Speculation in NLP: A Survey, Corpora, Methods, and Applications
## 3 Methods and Techniques
### Supervised Techniques  
In the past decade, interest has increased in the automatic detection of negation and speculation using supervised machine learning approaches, especially in English. Nevertheless, the lack of annotated corpora for the other natural languages has impeded the development of such approaches. This subsection presents the commonly used supervised learning approaches for negation and speculation detection, and Table 3 summarizes these approaches. [PERSON] and [PERSON] treated speculation detection as a binary classification problem and proposed a weakly supervised machine learning model [83]. Their support vector machine (SVM)-based model depends on a representation approach using a manually labeled corpus' bag-of-words (BoW). In 2008, [PERSON] adapted this model [83] by manually limiting the feature set and including bigrams and trigrams into the feature representation [84]. The weakly supervised models considered the entire sentence as either speculative or not speculative. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Ref.**} & \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Language**} & \multicolumn{2}{c}{**Negation**} & \multicolumn{2}{c}{**Speculation**} \\ \cline{3-6}  & & & **Features** & **Methods** & **Features** & **Methods** \\ \hline
[83] & 2007 & English & & & BoW & SVM \\
[84] & 2007 & English & & Bigrams, Trigrams & Weakly Supervised Learning \\
[72] & 2009 & English & PoS, Lemma, & SVM, CRF, & & \\  & & Syntactic & TiMBL & PoS, Lemma, & SVM-CRF-TiMBL \\
[73] & 2009 & English & & & BoW, PoS, Syntactic & SVM \\
[75] & 2009 & English & & PoS & CRF & \\
[44] & 2010 & English & & & Syntactic & SVM \\
[85] & 2012 & English & & SVM Tree & Syntactic & SVM Tree Kernel \\
[86] & 2013 & English & Syntactic & Kernel & Handcrafted Rules & MRS Crawler \\
[76] & 2014 & English & & Handcrafted Rules & MRS Crawler \\
[87] & 2016 & English & Syntactic, Cue & CNN & Syntactic, Cue & CNN \\
[88] & 2016 & English & WE & BiLSTM & & \\
[89] & 2016 & English & WE, PoS & BiLSTM & & \\
[90] & 2017 & English & & & WE & CNN with attention \\
[91] & 2017 & English & Lexical, Syntactic & SVM+CRF & & \\
[46] & 2017 & Chinese & Embedding, BoW, & CRF & & \\  & & BoC & & & \\
[92] & 2018 & English, & Latent Structural, & Semi-CRF & & \\
[93] & 2018 & Spanish & WE, PoS & LSTM & & \\
[94] & 2018 & English, & PoS & BiLSTM & & \\
[74] & 2019 & English & Cue & NegBERT & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._ \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Ref.**} & \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Language**} & \multicolumn{2}{c}{**Negation**} & \multicolumn{2}{c}{**Speculation**} \\ \cline{3-6}  & & & **Features** & **Methods** & **Features** & **Methods** \\ \hline
[11] & 2019 & English, Chinese & BERT, PoS & BiLSTM & BERT, PoS & BiLSTM \\
[24] & 2019 & French & WE, PoS & BiLSTM-CRF & WE, PoS & BiLSTM-CRF \\
[95] & 2019 & Arabic & & Syntactic, PoS & GAN with Attention \\
[96] & 2020 & English, Chinese & Syntactic & RNN-CRF & Syntactic & RNN-CRF \\
[39] & 2020 & English & Syntactic & BiLSTM \& & \\
[97] & 2020 & English & Syntactic & CNN & & \\
[97] & 2020 & English & Syntactic & STRNN & & \\
[13] & 2020 & Brazil, & WE, PoS & BiLSTM-CRF & & \\  & Portuguese & & & & \\
[36] & 2021 & Arabic & WE, Cue & BiLSTM & & \\
[17] & 2021 & Mexican & PoS, Cue & CRF & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Supervised techniques for negation and speculation detection. Since 2008, with the construction of the BioScope corpus [14], the scope detection problem began to be extensively solved using supervised machine learning approaches [14]. [PERSON] and [PERSON] modeled the negation scope detection problem as two consecutive classification tasks: task 1 detects the negation signal, and task 2 finds the scope of the detected negation signal [72].