Deep continual learning for medical call incidents text classification under the presence of dataset shifts.

The aim of this work is to develop and evaluate a deep classifier that can effectively prioritize Emergency Medical Call Incidents (EMCI) according to their life-threatening level under the presence of dataset shifts. We utilized a dataset consisting of 1982746 independent EMCI instances obtained from the Health Services Department of the Region of Valencia (Spain), with a time span from 2009 to 2019 (excluding 2013). The dataset includes free text dispatcher observations recorded during the call, as well as a binary variable indicating whether the event was life-threatening. To evaluate the presence of dataset shifts, we examined prior probability shifts, covariate shifts, and concept shifts. Subsequently, we designed and implemented four deep Continual Learning (CL) strategies-cumulative learning, continual fine-tuning, experience replay, and synaptic intelligence-alongside three deep CL baselines-joint training, static approach, and single fine-tuning-based on DistilBERT models. Our results demonstrated evidence of prior probability shifts, covariate shifts, and concept shifts in the data. Applying CL techniques had a statistically significant (α=0.05) positive impact on both backward and forward knowledge transfer, as measured by the F1-score, compared to non-continual approaches. We can argue that the utilization of CL techniques in the context of EMCI is effective in adapting deep learning classifiers to changes in data distributions, thereby maintaining the stability of model performance over time. To our knowledge, this study represents the first exploration of a CL approach using real EMCI data.

The Emotions of Abstract Words: A Distributional Semantic Analysis.

Recent psycholinguistic and neuroscientific research has emphasized the crucial role of emotions for abstract words, which would be grounded by affective experience, instead of a sensorimotor one. The hypothesis of affective embodiment has been proposed as an alternative to the idea that abstract words are linguistically coded and that linguistic processing plays a key role in their acquisition and processing. In this paper, we use distributional semantic models to explore the complex interplay between linguistic and affective information in the representation of abstract words. Distributional analyses on Italian norming data show that abstract words have more affective content and tend to co-occur with contexts with higher emotive values, according to affective statistical indices estimated in terms of distributional similarity with a restricted number of seed words strongly associated with a set of basic emotions. Therefore, the strong affective content of abstract words might just be an indirect byproduct of co-occurrence statistics. This is consistent with a version of representational pluralism in which concepts that are fully embodied either at the sensorimotor or at the affective level live side-by-side with concepts only indirectly embodied via their linguistic associations with other embodied words.