Investigating Stylistic Profiles for the Task of Empathy Classification in Medical Narrative Essays

TL;DR

This paper explores the use of linguistic constructions from Construction Grammar and Systemic Functional Grammar within deep learning models to improve empathy classification in medical narrative essays.

Contribution

It introduces a novel approach that integrates linguistic theories into deep learning models for empathy detection in medical narratives.

Findings

Linguistic constructions enhance empathy classification accuracy.

Enriched models outperform baseline neural networks.

Linguistic features contribute significantly to empathy profiling.

Abstract

One important aspect of language is how speakers generate utterances and texts to convey their intended meanings. In this paper, we bring various aspects of the Construction Grammar (CxG) and the Systemic Functional Grammar (SFG) theories in a deep learning computational framework to model empathic language. Our corpus consists of 440 essays written by premed students as narrated simulated patient-doctor interactions. We start with baseline classifiers (state-of-the-art recurrent neural networks and transformer models). Then, we enrich these models with a set of linguistic constructions proving the importance of this novel approach to the task of empathy classification for this dataset. Our results indicate the potential of such constructions to contribute to the overall empathy profile of first-person narrative essays.