Explaining Contextualized Word Embeddings in Biomedical Research – A Qualitative Investigation...International Conference on Informatics, Management, and Technology in Healthcare (ICIMTH), 1-3 July, 2022, Athens, Greece.
Contextualized word embeddings proved to be highly successful quantitative representations of words that allow to efficiently solve various tasks such as clinical entity normalization in unstructured texts. In this paper, we investigate how the Saussurean sign theory can be used as a qualitative exp...
| Publicado en: | Studies in Health Technology & Informatics Vol. 295; pp. 289 - 293 |
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| Autores principales: | , |
| Formato: | proceedings research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
2022
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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=ccm&AN=157849127&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 157849127 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09269630 U1V jtl: Studies in Health Technology & Informatics issn: 09269630 maglogo: N pubinfo: dt: 2022 vid: 295 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 157849127 157849127 157849127 10.3233/SHTI220719 157849127 ppf: 289 ppct: 4 formats: tig: atl: Explaining Contextualized Word Embeddings in Biomedical Research – A Qualitative Investigation...International Conference on Informatics, Management, and Technology in Healthcare (ICIMTH), 1-3 July, 2022, Athens, Greece. aug: au: MILETIC, Marko SARIYAR, Murat affil: Bern University of Applied Sciences, Switzerland. sug: subj: Language Artificial Intelligence Utilization Task Performance and Analysis Biomedical Engineering Human Qualitative Studies Goals and Objectives Confidence Trust Natural Language Processing Models, Theoretical ab: Contextualized word embeddings proved to be highly successful quantitative representations of words that allow to efficiently solve various tasks such as clinical entity normalization in unstructured texts. In this paper, we investigate how the Saussurean sign theory can be used as a qualitative explainable AI method for word embeddings. Our assumption is that the main goal of XAI is to produce confidence and/or trust, which can be gained through quantitative as well as quantitative approaches. One important result is related to the fact that the differential structure of language as explained by Saussure corresponds to the possibility of adding and subtracting word embeddings. On the other hand, these mathematical structures provide insights into the inner workings of natural language. pubtype: Academic Journal doctype: proceedings research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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