# Negation and Speculation in NLP: A Survey, Corpora, Methods, and Applications
## 3 Methods and Techniques
### Supervised Techniques
used the BiLSTM model, producing in- and out-of-scope tokens within a sentence to identify the negation scope in the SEM 2012* corpus [89]. The word embedding and PoS cue feature fed into the BiLSTM network to detect the negation scope of this cue. This approach modeled the negation scope detection as independent predictions for each token. In English, negation scope boundaries often depend on punctuation, especially in old-style writing as in [PERSON]'s short stories. [PERSON] et al. tried to alleviate the effect of this issue with a dependency-based BiLSTM model that processes dependency trees using encodings for words and dependency relations [94]. The authors developed a set of models based on English, with a cross-lingual word representation and universal PoS features, yet then applied them to Chinese. These models were evaluated on the NegPar corpus [15] using several performance measures, but some cross-lingual features related to negation scope are still missing. BiLSTM-based models were also used for negation and speculation scope detection in languages other than English. [PERSON] et al. adapted the proposed model by [PERSON] [89] to detect negation cues in the Spanish reviews domain [93]. This model used the encodedtokens and lemmas generated by a pre-trained Spanish word embedding model in addition to the PoS feature. [PERSON] et al. proposed a BiLSTM model with three dense layers and a single output layer to detect negation scope in Arabic texts [36]. This network takes an input vector of the FastText word embeddings representation and another vector of the cues features to identify the scope of a negation cue within a sentence. The results show that BiLSTM-based models with word embedding representation achieve a remarkable performance in English, Arabic, and Spanish. Without any handcrafted or highly engineered features, the experimental results of RNNs and CNNs-based models outperform feature-rich SVM and CRF-based models. Nonetheless, the researchers combined RNN/CNN networks and CRF-based models in some studies. [PERSON] et al. used RNN and CRFs, RecurCRFs, as a negation and speculation scope detection framework [96]. At first, the RNN model learns a high-level representation of each sentence and detects negating or speculative words with their scope through the dependency tree structure, where an embedding vector represents each word in the input sentence. After that, a linear chain of CRFs layer takes this representation from RNN as input to decode each label. The authors assumed in this work that the negation and speculation scopes impact each other. This framework was tested on two corpora from separate languages: BioScope [14] and CNeSp [43]. Each covers annotated data from different domains. This framework had a generalization across languages and domains. Therefore, [PERSON] et al. designed a cross-domain approach to identify the negation in French and Brazilian Portuguese [13]. In their study, they built word-embedding models for the French language that were composed of French Wikipedia articles and biomedical data. For the Brazilian Portuguese, they used pre-trained models from the NILC website. In addition, various supervision-based models were adapted from Fancellu [89], BiLSTM, BiGRU, and BiLSTM-CRF to detect the negation cue and its scope automatically. The BiLSTM-CRF-based model achieved the best performance in the French and Brazilian Portuguese cross-domain experiments. By contrast, the French cross-domain result for the ESSAI-CAS corpora had a low value for exact matching scope detection, because the CAS corpus was automatically annotated. In another study, syntactic path-based hybrid architecture combines the BiLSTM and CNN networks [39]. The BiLSTM learns a sentence's context representation by encoding the semantics of each word, while the CNN network captures the syntactic features between the negation cue and its surroundings using dependency parse trees.