Publication:

Automated Evidence Extraction and Scoring for Corporate Climate Policy Engagement: A Multilingual RAG Approach

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2025
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cris.virtual.orcid0000-0001-8029-4106
cris.virtual.orcid0000-0001-5983-2360
cris.virtualsource.orcid5b89af53-a78a-4012-bb2a-48096581f6e3
cris.virtualsource.orcid0331cda6-e903-4e22-9b44-f89f54f581dc
dc.date.accessioned2025-12-03T13:31:12Z
dc.date.available2025-12-03T13:31:12Z
dc.date.issued2025-11-09
dc.identifier.doi10.18653/v1/2025.emnlp-demos.9
dc.identifier.urihttps://www.zora.uzh.ch/handle/20.500.14742/238928
dc.language.isoeng
dc.sourceCrossref:10.18653/v1/2025.emnlp-demos.9
dc.subject.ddc330 Economics
dc.title

Automated Evidence Extraction and Scoring for Corporate Climate Policy Engagement: A Multilingual RAG Approach

dc.typeconference_item
dcterms.accessRightsinfo:eu-repo/semantics/openAccess
dcterms.bibliographicCitation.originalpublishernameAssociation for Computational Linguistics
dcterms.bibliographicCitation.pageend129
dcterms.bibliographicCitation.pagestart111
dspace.entity.typePublication
oairecerif.event.endDate2025-11-09
oairecerif.event.placeSuzhou, China
oairecerif.event.startDate2025-11-04
uzh.contributor.authorKolli, Imene
uzh.contributor.authorVaghefi, Saeid
uzh.contributor.authorColesanti Senni, Chiara
uzh.contributor.authorRaj, Shantam
uzh.contributor.authorLeippold, Markus
uzh.contributor.editorHabernal, Ivan
uzh.contributor.editorSchulam, Peter
uzh.contributor.editorTiedemann, Jörg
uzh.contributor.editoremail#PLACEHOLDER_PARENT_METADATA_VALUE#
uzh.contributor.editoremail#PLACEHOLDER_PARENT_METADATA_VALUE#
uzh.contributor.editoremail#PLACEHOLDER_PARENT_METADATA_VALUE#
uzh.document.availabilitypublished_version
uzh.event.presentationTypePaper
uzh.event.titleThe 2025 Conference on Empirical Methods in Natural Language Processing
uzh.event.typeKonferenzConference
uzh.identifier.doihttps://doi.org/10.5167/uzh-281115
uzh.oastatus.zoraGreen
uzh.publication.citationKolli, I., Vaghefi, S., Colesanti Senni, C., Raj, S., & Leippold, M. (2025). Automated Evidence Extraction and Scoring for Corporate Climate Policy Engagement: A Multilingual RAG Approach (I. Habernal, P. Schulam, & J. Tiedemann, Eds.; pp. 111–129). Association for Computational Linguistics. https://doi.org/10.18653/v1/2025.emnlp-demos.9
uzh.publication.freeAccessAtUNSPECIFIED
uzh.publication.originalworkoriginal
uzh.publication.publishedStatusfinal
uzh.publication.scopedisciplinebased
uzh.publication.seriesTitleProceedings of the Conference on Empirical Methods in Natural Language Processing: System Demonstrations
uzh.workflow.chairSubjectoecIBF1
uzh.workflow.fulltextStatuspublic
uzh.workflow.rightsCheckoffen
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