# Negation Detection on Mexican Spanish Tweets:
## 3 Corpus of Negations in Mexican Spanish: The T-MexNeg
### Annotation Methodology  
Based on the description of the above annotation tags, we created an annotation guide that specifies how the tweets should be labeled and under which criteria (the annotation guidelines are publicly available at: [[https://gitlab.com/gil.iingen/negation_twitter_mexican_spanish/-/blob/master/annotation_guidelines.pdf](https://gitlab.com/gil.iingen/negation_twitter_mexican_spanish/-/blob/master/annotation_guidelines.pdf)]([https://gitlab.com/gil.iingen/negation_twitter_mexican_spanish/-/blob/master/annotation_guidelines.pdf](https://gitlab.com/gil.iingen/negation_twitter_mexican_spanish/-/blob/master/annotation_guidelines.pdf)); accessed on 12 February 2021). We performed a manual tagging process for labeling each of the tweets in the corpus. The tagging process involved three teams of two annotators. All annotators were linguistics students, and we had a chief linguist who helped to solve cases where the linguist annotators disagreed. Each team tagged the full dataset using the Dataturks ( [[https://dataturks.com/](https://dataturks.com/)]([https://dataturks.com/](https://dataturks.com/)); accessed on 12 February 2021) online platform, which allows this type of annotation for computational purposes. Thus, for each tweet, we obtained three different annotations.  
To facilitate the handling of the tagged corpus, we applied a transformation of the original JSON file that was obtained from Dataturks to XML. The general structure to which the tagged corpus was transformed is shown in Figure 2. The full corpus is freely available and can be used in different natural language processing pipelines ([[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 12 February 2021).  
With the tweets already tagged, we computed the statistics regarding the word counts for each tweet in the corpus. Table 3 shows the general statistics for both types of tweets, with and without negation; therefore, we can observe that, in both cases, tweets have a minimum of 1 word and a maximum of 29 words, so that independent of presence or absence of negation, tweets in our corpus range from 1 to 29 words. We can also observe in the table that tweets that contain negations tend to be almost three words longer on average than a regular tweet, however, the standard deviation of the number of tokens is quite high for the length to be a reliable distinctive feature. We also reviewed which were the most used tags and which types of negation cues are the most common. Table 4 shows the negation cue tags distribution (Freq) along with some statistics such as the average, minimum, and the maximum number of words in each tag. As can be seen, most of the negation cues are simple, while the number of related and false negation (no_neg) are very similar. Table 4 also shows the frequency of the scope and event tag, where it can be observed that the average number of words within the scope is larger than in the rest of the tags. This variation is expected given that the scope corresponds to all words affected by the negation. Concerning the event tag, it is typically composed of only one word (average); however, there can be more than one word that is specifically negated. Note that, since we did not perform any pre-processing of the tweets, there are several variations of every word, including capitalization, emphasis by the repetition of a vowel, and other phenomena. This can be seen in Appendixes A-C.  
Figure 2: General structure of the negation tagging.