# Negation and Speculation in NLP: A Survey, Corpora, Methods, and Applications
## 3 Methods and Techniques
### Supervised Techniques
A memory-based learning algorithm, Tilburg Memory-Based Learner (TiMBL) ([[https://languagemachines.github.io/timbl/](https://languagemachines.github.io/timbl/)]([https://languagemachines.github.io/timbl/](https://languagemachines.github.io/timbl/)) (accessed on 20 March 2022)), was used with features such as PoS and lemma of the token to identify the negation signals. In the second phase, the TiMBL, SVM, and conditional random field (CRF) classifiers were combined with shallow syntactic features to pass their predictions to a metalearner CRF classifier to predict the negation scope. In another study [73], the authors implemented the same approach to find the scope of speculation cues in biomedical texts. Although this system is portable for similar negating and speculative detection, it depends on a highly engineered features set. [PERSON] et al. proposed a tree kernel classifier based on the constituent and dependency structured syntactic features, proving their efficiency in capturing the cues' relation and scope [86]. This system considered the problem a binary classification task, where the SVM classifier used a kernel function between two trees to classify whether the token lay inside or outside the scope. The first joint conference on lexical and computational semantics (SEM 2012*) published an annotated corpus with negation to prompt for systems to detect negation cues, scope, and negated events [58]. Of the 12 participating teams, the first to submit a system was from the University of Oslo (UiO1), and it was selected as the best-performing system for this task [85]. As in previous studies, an SVM classifier was used, but it depends on syntactic constituents generated by following the path from the negation cue to the root of the syntactic tree. In this approach, [PERSON] et al. tried to handle the discontinuous scope of negation by removing unnecessary words from the negation scope. Even though their system achieved the top-ranked system with an F1-score of 78, it is a highly engineered system and in the case of a single constituent, fails to capture discontinuous scope. [PERSON] et al. developed a simple open-source Python-based tool ([[https://github.com/marenger/negtool](https://github.com/marenger/negtool)]([https://github.com/marenger/negtool](https://github.com/marenger/negtool)) (accessed on 18 March 2022)) to identify negation scope using an approach called maximum-margin [91]. This approach divides the problem into two sub-tasks; the cue detection task uses lexical features with a binary SVM classifier. The second sub-task, scope resolution, is a sequence-labeling task that uses syntactic-based features with a linear-chain CRF model; however, like (UiO1), it failed to handle discontinuous scope [85]. Other studies tackled the problem of scope detection as a sequential labeling task, using CRF-based models. For example, [PERSON] and [PERSON] proposed a semi-CRF-based model ([[https://github.com/leodotnet/negationscope](https://github.com/leodotnet/negationscope)]([https://github.com/leodotnet/negationscope](https://github.com/leodotnet/negationscope)) (accessed on 3 March 2022)) to identify negation scope in the SEM 2012* corpus [92]. The features set includes long-distance dependencies, latent structural information, and negation cue-related features such as the relative positions of each token and cue, as in another study [98]. Their model was designed to identify the negation scope at the span level to capture discontinuous and partial scope, and another latent-based CRF model was used to represent the implicit patterns. As a result, this system outperformed the best performing system (UiO1) at SEM 2012* [85]. CRF-based models were used to detect negation scope in other natural languages. [PERSON] et al. used a CRF model with character and word embedding for Chinese clinical notes to identify negation scope [46]. Besides the character and word embedding, a feature known as 'In Previous Scopes' was added to handle the nested negation scope within multi-cue sentences.