Investigating Stylistic Profiles for the Task of Empathy Classification in Medical Narrative Essays
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.
Keywords
Cite
@article{arxiv.2302.01839,
title = {Investigating Stylistic Profiles for the Task of Empathy Classification in Medical Narrative Essays},
author = {Priyanka Dey and Roxana Girju},
journal= {arXiv preprint arXiv:2302.01839},
year = {2023}
}
Comments
12 pages, 5 figures; This paper will appear in the ACL Anthology (Association for Computational Linguistics) and will be presented at the Construction Grammars and NLP (CxGs+NLP) Workshop, the Georgetown University Round Table (GURT), March 2023