# Negation Detection on Mexican Spanish Tweets:
## 2 Related Work
### Methods and Shared Task for Negation Identification  
The development of systems that automatically detect negation is a well-known natural language processing task. This task is usually divided into four fundamental assignments: (I) negation cue detection; (II) negation event recognition; (III) negation scope detection; and (IV) focus detection ([PERSON] et al., 2018). There are essentially three different approaches applied to the development of these systems. First, rule-based systems (NegEx, ConText, DEEPEN, NegMiner, etc.) emerged from the need to automatically extract information from clinical records. The NegEx system, developed by [PERSON] et al. (2018), is one of the most popular systems, and it uses a list of triggers that indicate the presence of negation. Later, other techniques emerged such as ConText ([PERSON] et al., 2018), DEEPEN ([PERSON] et al., 2018), and NegMiner ([PERSON] et al., 2018). [PERSON] et al. (2018) adapted and translated NegEx into Spanish and used it to detect negation in clinical texts. Second, machine learning techniques have had exponential growth in the last decade. [PERSON] and [PERSON] (2018) developed a classifier based on a Conditional Random Field algorithm to automatically identify negation and its scope in biological and clinical text. [PERSON] et al. (2018) proposed a semantic parsing approach to learn the scope of negation. [PERSON] et al. (2018) implemented a Naive Bayes classifier that identifies negation and speculation signals and their scope in clinical texts. [PERSON] et al. (2018) presented a machine learning system based on a Conditional Random Field that processes negation in Spanish. They focused on two tasks, negation cue detection, and scope identification, outperforming state-of-the-art results for cue detection and being the first system that performs the task of scope detection for Spanish. Third, the most recent approaches suggest the use of deep learning techniques. [PERSON] et al. (2018) used neural network architectures to automatically detect negation scope. [PERSON] et al. (2018) proposed a Convolutional Neural Network-based model to address speculation and negation scope detection. Recently, [PERSON] and [PERSON] (2018) utilized transformer-based architectures such as BERT, XLNet, and RoBERTa to detect negation and speculation. It is important to highlight that none of the previous works on negation detection explore the domain of social media. Given that social media is nowadays one of the main communication platforms and attracts research on different natural language processing tasks, it is important to have corpora for exploring automatic methods for negation detection. In another vein, some works deal with negation on social media in the context of sentiment analysis. Two key works in this line of research were performed by [PERSON] et al. (2018) and [PERSON] et al. (2018). The former labels with the \"_NEG\" suffix every word from the negation cue to a punctuation mark and shows how this strategy improves the performance of the system for detecting polarity. The latter approaches several methods \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Corpus** & **Year** & **Domain** & **Scope** & **Event** & **Number of Sentences** & **Annotated Negations** \\ \hline SFU Review\({}_{\text{SP}}\)-NEG & 2018 & Reviews & Yes & Yes & 9455 & 3022 \\ UAM Spanish Treebank & 2013 & Newspaper & Yes & No & 1501 & 160 \\ NewsCom & 2020 & Comments & Yes & Yes & 4980 & 2247 \\ IxadMed-GS & 2015 & Medical & No & Yes & 5410 & 763 \\ UHU-HUVR & 2017 & Medical & Yes & Yes & 8412 & 1079 \\ IULA Spanish Clinical report & 2017 & Medical & Yes & No & 3194 & 1093 \\ NUBES & 2020 & Medical & Yes & Yes & 29,682 & 7567 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Spanish corpora annotated with negation. for approaching modeling of negation in sentiment analysis. However, these works are mainly focused on the detection of polarity and do not tackle the structure of negation. Several shared tasks have emerged to tackle NLP problems regarding negation.