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        <title>Investigating Stylistic Profiles for the Task of Empathy Classification in Medical Narrative Essays</title>
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            <title>Proceedings of the First International Workshop on Construction Grammars and NLP (CxGs+NLP, GURT/SyntaxFest 2023)</title>
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            <namePart type="given">Claire</namePart>
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            <namePart type="given">Harish</namePart>
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    <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.</abstract>
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        <url>https://aclanthology.org/2023.cxgsnlp-1.8</url>
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%0 Conference Proceedings
%T Investigating Stylistic Profiles for the Task of Empathy Classification in Medical Narrative Essays
%A Dey, Priyanka
%A Girju, Roxana
%Y Bonial, Claire
%Y Tayyar Madabushi, Harish
%S Proceedings of the First International Workshop on Construction Grammars and NLP (CxGs+NLP, GURT/SyntaxFest 2023)
%D 2023
%8 March
%I Association for Computational Linguistics
%C Washington, D.C.
%F dey-girju-2023-investigating
%X 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.
%U https://aclanthology.org/2023.cxgsnlp-1.8
%P 63-74

Markdown (Informal)

[Investigating Stylistic Profiles for the Task of Empathy Classification in Medical Narrative Essays](https://aclanthology.org/2023.cxgsnlp-1.8) (Dey & Girju, CxGsNLP-SyntaxFest 2023)

• Investigating Stylistic Profiles for the Task of Empathy Classification in Medical Narrative Essays (Dey & Girju, CxGsNLP-SyntaxFest 2023)

ACL

• Priyanka Dey and Roxana Girju. 2023. Investigating Stylistic Profiles for the Task of Empathy Classification in Medical Narrative Essays. In Proceedings of the First International Workshop on Construction Grammars and NLP (CxGs+NLP, GURT/SyntaxFest 2023), pages 63–74, Washington, D.C.. Association for Computational Linguistics.