EliXa: A modular and flexible ABSA platform
Table 2: Results obtained on the slot2 evaluation on restaurant data.
['[BOLD] System (type)', '[BOLD] Precision', '[BOLD] Recall', '[BOLD] F1 score']
The implementation of the clustering features looks for the cluster class of the incoming token in one or more of the clustering lexicons induced following the three methods listed above. If found, then we add the class as a feature. The Brown clusters only apply to the token related features, which are duplicated. We chose the best combination of features using 5-fold cross validation, obtaining 73.03 F1 score with local features (e.g. constrained mode) and 77.12 adding the word clustering features, namely, in unconstrained mode. These two configurations were used to process the test set in this task.