Advancing NLP with Cognitive Language Processing Signals
Table 1: Precision (P), recall (R) and F1-score (F1) for the four tasks augmented with gaze features, EEG features, and both. Significance is indicated with the asterisks: * = p<0.01, ** = p<0.0008 (Bonferroni method).
['[EMPTY]', '[BOLD] NER P', '[BOLD] NER R', '[BOLD] NER F1', '[BOLD] RelClass P', '[BOLD] RelClass R', '[BOLD] RelClass F1', '[BOLD] Sentiment (2) P', '[BOLD] Sentiment (2) R', '[BOLD] Sentiment (2) F1', '[BOLD] Sentiment (3) P', '[BOLD] Sentiment (3) R', '[BOLD] Sentiment (3) F1']
We observe consistent improvements over the baselines for all tasks when augmented with cognitive features. The models with gaze features, EEG features and the combination thereof all outperform the baseline. Notably, while the combination of gaze and EEG features also outperform the baseline, they do not improve over using gaze or EEG individually.