# Negation and Speculation in NLP: A Survey, Corpora, Methods, and Applications
## 5 Discussion
Although RNNs, CNNs, and CRF-based systems with the data's syntactic structure achieved better performance than traditional machine learning techniques, their lack of annotated data limited the progress in treating negation. Possible solutions to redress the imbalance for low-resource languages include merging multiple corpora of the same language, translating the well-known English corpora such as BioScope [14] and SFU Review [40], into other languages, or transferring the models cross-lingually. When transfers between Spanish and Mexican Spanish [17] and between French and Brazilian Portuguese [13] were evaluated, this approach seemed to have resolved the problem somewhat. As with corpora for negation, speculation corpora include cue, scope, and events elements, but they are less available. Most were annotated for the clinical and biomedical domain, and it is rare to find a corpus annotated for speculation in other domains, such as for English reviews [40] and Chinese literature or financial articles [43]. A trial to handle the detection of speculative scope uses a modified version of NegEx on German biomedical data, but it records low performance [61]. As seen in our survey, there are no rule-based algorithms to have been developed to handle the problem of speculation. However, the supervised learning techniques that have been used for the detection of negation also achieved good results on the problem of speculation [86; 96]. The previous studies addressed the negation and speculation phenomena utilizing a variety of methodologies. However, there are still certain challenges that have not been addressed. Among the challenges are the ambiguity of cues and instances in which a simple cue is part of a discontinuous cue or scope [35; 36]. In addition, poor handling of discontinuous scopes and implicit negations and speculations have not been addressed [5]. In order to determine the patterns of the nontrivial cases of negation and speculation, additional research is required. Taking into account more sophisticated and syntactic features may be useful for detecting the discontinuous and implicit negation and speculation. Additionally, recent embedding techniques, like BERT [99] and ELMo [101], may provide a more precise representation of the negating and speculative sentences. Some research teams adopted transfer learning-based systems with pre-trained models, achieving better results than with other supervised learning techniques [74]. Section 4 discusses the importance in multiple NLP tasks of determining both negating and speculative clauses within a sentence. For example, it is crucial to detect peculation in the task of SA to differentiate between the objective and subjective content, yet insufficient studies have addressed this issue. Furthermore, any inappropriate handling of negation leads to the SA task generating false results. In the biomedical domain, spotting negation and speculation can save people's lives by identifying those patients at high risk of suicide [125]. Negation and speculation detection systems should be incorporated into the pipeline of NLP tasks, including SA and IE in the biomedical domain.