# Negation and Speculation in NLP: A Survey, Corpora, Methods, and Applications
## 2 Corpora and Annotation Process
### Corpora
#### 2.2.1 English Corpora
The authors enriched the IULA corpus by incorporating uncertainty and have made the guidelines publicly available ([[https://github.com/Vicomtech/NUBes-negation-uncertainty-biomedical-corpus](https://github.com/Vicomtech/NUBes-negation-uncertainty-biomedical-corpus)]([https://github.com/Vicomtech/NUBes-negation-uncertainty-biomedical-corpus](https://github.com/Vicomtech/NUBes-negation-uncertainty-biomedical-corpus)) (accessed on 20 January 2022)). Spanish: NewsComm was the first Spanish corpus to have been collected from newspapers and annotated for negation phenomena. Also, it is considered to be the first Spanish corpus to be manually annotated with negation alongside negation cues and scope. The corpus consists of 2955 comments posted in response to 18 news articles on nine topics (immigration, politics, technology, terrorism, economy, society, religion, refugees, and real estate) in an online Spanish newspaper, two articles per topic [35]. Linguistic analysis of the negation focus arrived at 10 conditions for the various forms of negation in this corpus. The criteria for annotation are described in detail in the guidelines ([[http://clic.ub.edu/publications](http://clic.ub.edu/publications)]([http://clic.ub.edu/publications](http://clic.ub.edu/publications)) (accessed on 25 February 2022)). The two trained annotators selected to annotate this corpus manually had earlier annotated the SFU Review\({}_{\text{SP}}\) NEG corpus [26]. They found 2965 negating structures in the corpus, with corresponding negation cues, scope, and focus. The result of the annotation process shows that 45% (2247 of 4980 sentences) are negating. Furthermore, the kappa IAA measure is 0.83, a high value for this first annotated Spanish corpus to have a negation focus. The authors made the corpus freely available for research purposes. Mexican Spanish: T-MexNeg is the first Mexican Spanish corpus annotated for negating phenomena [17]. It consists of 13,704 tweets collected from September 2017 to April 2019 using the standard streaming APIs from Twitter ([[https://developer.twitter.com/en/docs/tutorials/consuming-streaming-data](https://developer.twitter.com/en/docs/tutorials/consuming-streaming-data)]([https://developer.twitter.com/en/docs/tutorials/consuming-streaming-data](https://developer.twitter.com/en/docs/tutorials/consuming-streaming-data)) (accessed on 1 March 2022)). To limit the collection to Mexico, tweets were filtered by the language tag 'es' and the user location'mx'. Although the corpus is very large, it was annotated manually for negation cue, scope, and event. In addition, the authors adapted the SFU ReviewSP NEG [26] guidelines to Mexican Spanish and the nature of the collected corpus. The annotation process was carried out in two stages: binary classification for the presence of negation, and manual annotation by three teams of two annotators, who were linguistics students, together with a linguist. As a result, the T-MexNeg corpus of 13,704 sentences includes 4895 tweets with negation. The IAA was measured with [PERSON]'s kappa coefficient and has a value of 0.89, which is relatively high. The authors made this corpus publicly available for research purposes ([[https://gitlab.com/gil.iingen/negation_twitter_mexican_spanish](https://gitlab.com/gil.iingen/negation_twitter_mexican_spanish)]([https://gitlab.com/gil.iingen/negation_twitter_mexican_spanish](https://gitlab.com/gil.iingen/negation_twitter_mexican_spanish)) (accessed on 1 March 2022)). Arabic: The ArNeg corpus is another Arabic corpus that has been annotated with negation for formal Arabic texts [36]. It consists of 6000 sentences collected from Wikipedia and the King Saud University-Corpus for Classical Arabic (KSUCCA). The corpus covers sentences from topics such as biography, media, science, and technology. [PERSON] et al. wrote clear annotation guidelines, to which the two independent Arabic native speakers adhered in the annotation process. The percentage of the negated sentences was found to be 18% for the Wikipedia sub-corpus and 29% for the KSUCCA sub-corpus.