English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

One important aspect of language is how speakers generate utterances andtexts to convey their intended meanings. In this paper, we bring variousaspects of the Construction Grammar (CxG) and the Systemic Functional Grammar(SFG) theories in a deep learning computational framework to model empathiclanguage. Our corpus consists of 440 essays written by premed students asnarrated simulated patient-doctor interactions. We start with baselineclassifiers (state-of-the-art recurrent neural networks and transformermodels). Then, we enrich these models with a set of linguistic constructionsproving the importance of this novel approach to the task of empathyclassification for this dataset. Our results indicate the potential of suchconstructions to contribute to the overall empathy profile of first-personnarrative essays.

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