MedicalCare: building and annotating an empathy-rich corpus.
The importance of empathy in clinical settings has been widely accepted in the research community, and there have been numerous attempts at training clinical practitioners in empathic communication. Despite the advances in affective computing and automatic recognition and classification of emotions...
| Publicado en: | Language Resources & Evaluation Vol. 59; no. 3; pp. 2329 - 2365 |
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| Autores principales: | , , , , , |
| Formato: | Artículo |
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Springer Nature
Sep2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=186909065&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 186909065 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 1574020X 179V jtl: Language Resources & Evaluation issn: 1574020X maglogo: N pubinfo: dt: Sep2025 vid: 59 iid: 3 pid: 237 pub: Springer Nature artinfo: ui: 186909065 10.1007/s10579-025-09806-7 ppf: 2329 ppct: 36 formats: fmt: – @attributes: type: T – @attributes: type: P size: 1.4MB tig: atl: MedicalCare: building and annotating an empathy-rich corpus. aug: au: Sun, Yinglun Zavala, Jose Shi, Shuju Finegold, Rachel Girju, Roxana Moore, Jeffrey affil: https://ror.org/047426m28 Department of Linguistics, University of Illinois at Urbana-Champaign, Champaign, USA https://ror.org/047426m28 Beckman Institute, University of Illinois at Urbana-Champaign, Champaign, USA https://ror.org/047426m28 Department of English, University of Illinois at Urbana-Champaign, Champaign, USA su: Empathy Affective computing Inter-observer reliability Corpora Content analysis Language models sug: subj: Empathy Affective computing Inter-observer reliability Corpora Content analysis Language models keyword: Annotation Communication and Culture Linguistics Corpus Language ab: The importance of empathy in clinical settings has been widely accepted in the research community, and there have been numerous attempts at training clinical practitioners in empathic communication. Despite the advances in affective computing and automatic recognition and classification of emotions in discourse, there has been little research on how to characterize and model empathy in clinical settings. A corpus of essays was collected as a preliminary dataset for building an early stage linguistic model and measuring the efficacy of inter-annotator agreement on such a dataset. As annotated corpora have been popular resources for research on affective computing, in this study we build a text corpus named MedicalCare, and annotate it for empathic expressions using an iterative annotation process. We evaluated the annotation quality and the level of inter-annotator agreement over time, and found steady improvement in inter-annotator agreement on sentence labels as well as elaboration of the annotation guidelines. The average inter-rater agreement obtained over 370 essays annotated by four annotators is κ = 0.65, and κ = 0.82 between two meta-annotators. We also conducted text analyses of the annotated essays and found that the use of personal pronouns, negative emotion words and words about reassurance are correlated with empathic expressions. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Language Resources & Evaluation is a copyright of Springer, 2025. All Rights Reserved. item: Language Resources & Evaluation holder: Springer Nature dt: @attributes: year: 2025 holdings: @attributes: islocal: N |
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