# Negation Detection on Mexican Spanish Tweets:
## 5 Results  
This section shows the results of the experiments performed with the corpus. Table 6 summarizes the results of both dictionary-based system and CRF-based system on the task of negation detection on both corpora T-MexNeg (TMN) and SFU ReviewSP-NEG (SFU)). The CRF-based system was first trained and tested on the T-MexNeg corpus and then trained and tested on the SFU ReviewSP-NEG corpus. As we can see, both systems achieved F1-scores above 85% for each corpus, which is a consequence of the word _no_ (no) being the most used negation in both corpora. The negation cue _no_ (no) represent 78% of the total negation tags in the T-MexNeg corpus and 61% in the SFU ReviewSP-NEG corpus (see Figure 4). The result is an efficient detection of negation cues, minimizing the number of false positives. Figure 4 shows the 10 most frequent negations in both the SFU ReviewSP-NEG (orange) corpus and the T-MexNeg (blue) corpus. The CRF-based system gives better results on both corpora, improving the dictionary-based system. F1-score increased from 91% to 95% in the T-MexNeg corpus and from 86% to 88% in the SFU ReviewSP-NEG corpus. This is because the CRF-based system is flexible enough in cues of feature selection, cutting the limitations of the dictionary-based system, which only looks for the words in the dictionary. \begin{table}
\begin{tabular}{l c c c c} \hline \hline  & \multicolumn{2}{c}{**Experiment 1**} & \multicolumn{2}{c}{**Experiment 2**} \\  & \multicolumn{2}{c}{**Dictionary**} & \multicolumn{2}{c}{**CRF**} \\  & **TNM** & **SFU** & **TNM** & **SFU** \\ \hline Precision & 0.89 & 0.89 & 0.93 & 0.79 \\ Recall & 0.92 & 0.83 & 0.98 & 0.96 \\ F1 & 0.91 & 0.86 & 0.95 & 0.88 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results for negation cue detection using dictionary and CRF approaches on TNM and SFU corpora. Figure 4: Ten most frequent negation in both _T-MexNeg_ corpus (represented in blue) and _SFU ReviewSP-NEG_ corpus (represented in orange). Table 7 summarizes the results obtained in the task of identification of the negation cue, the negation event, the negation scope, and all three negation, event, and scope together. The first three columns of Table 7 represent the results when the CRF-based system was trained and tested on the T-MexNeg corpus using 10-fold cross-validation. The last three columns represent the results when the CRF-based system was trained and tested on the SFU ReviewSP-NEG corpus. These experiments aimed to test the same CRF-based system in two different corpora for different tasks (negation, event, and scope). As we can see, the highest F1 scores on both corpora were obtained in the task of identifying the negation cue. In the task of identification of the event, the F1-score is high for the T-MexNeg corpus (90%) but low for the SFU ReviewSP-NEG corpus (59%). This is because the structure of the negation event in the T-MexNeg corpus is shorter than it is in the SFU ReviewSP-NEG corpus, resulting in a harder task when working on the SFU ReviewSP-NEG corpus. On the other hand, in the task of identification of the scope, the F1-score reached 75% for the T-MexNeg corpus and 68% for the SFU ReviewSP-NEG corpus. This is a result of the length of the negation scope structure, which is large in both corpora. This complicates the task producing many false positives. Now, for the task of identification of all three negation, event, and scope together (GLOBAL), we obtained the lowest F1-scores out of all the experiments. Since the experiment demands the identification of three different tokens in the same sentence, a high number of errors is conceivable. Besides, considering that a true positive requires that all tokens in a sentence are correctly predicted, these errors will yield false positives resulting in a low Precision. In Table 8, we can see the results of the experiment when we trained the CRF-based system for the task of negation cue detection.