Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/106692
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dc.contributorDepartment of Chinese and Bilingual Studies-
dc.creatorQiu, L-
dc.creatorPeng, B-
dc.creatorGu, J-
dc.creatorHsu, YY-
dc.creatorChersoni, E-
dc.date.accessioned2024-06-03T02:11:32Z-
dc.date.available2024-06-03T02:11:32Z-
dc.identifier.isbn979-8-89176-024-0-
dc.identifier.urihttp://hdl.handle.net/10397/106692-
dc.descriptionThe Sixth Workshop on Financial Technology and Natural Language Processing, November 1, 2023, Bali, Indonesiaen_US
dc.language.isoenen_US
dc.publisherAssociation for Computational Linguisticsen_US
dc.rights©2023 Association for Computational Linguisticsen_US
dc.rightsACL materials are Copyright © 1963–2024 ACL; other materials are copyrighted by their respective copyright holders. Materials prior to 2016 here are licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 International License (https://creativecommons.org/licenses/by-nc-sa/3.0/). Permission is granted to make copies for the purposes of teaching and research. Materials published in or after 2016 are licensed on a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/).en_US
dc.rightsThe following publication Le Qiu, Bo Peng, Jinghang Gu, Yu-Yin Hsu, and Emmanuele Chersoni. 2023. Identifying ESG Impact with Key Information. In Proceedings of the Sixth Workshop on Financial Technology and Natural Language Processing, pages 51–56, Bali, Indonesia. Association for Computational Linguistics is available at https://doi.org/10.18653/v1/2023.finnlp-2.7.en_US
dc.titleIdentifying ESG impact with key informationen_US
dc.typeConference Paperen_US
dc.identifier.spage51-
dc.identifier.epage56-
dc.identifier.doi10.18653/v1/2023.finnlp-2.7-
dcterms.abstractThe paper presents a concise summary of our work for the ML-ESG-2 shared task, exclusively on the Chinese and English datasets. ML-ESG-2 aims to ascertain the influence of news articles on corporations, specifically from an ESG perspective. To this end, we generally explored the capability of key information for impact identification and experimented with various techniques at different levels. For instance, we attempted to incorporate important information at the word level with TF-IDF, at the sentence level with TextRank, and at the document level with summarization. The final results reveal that the one with GPT-4 for summarisation yields the best predictions.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationIn The Sixth Workshop on Financial Technology and Natural Language Processing : Proceedings of the Workshop, November 1, 2023, p. 51-56. Stroudsburg : Association for Computational Linguistics, 2023-
dcterms.issued2023-
dc.relation.ispartofbookThe Sixth Workshop on Financial Technology and Natural Language : Processing Proceedings of the Workshop, November 1, 2023-
dc.relation.conferenceWorkshop on Financial Technology and Natural Language Processing [FinNLP]-
dc.description.validate202405 bcch-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumbera2727aen_US
dc.identifier.SubFormID48137en_US
dc.description.fundingSourceSelf-fundeden_US
dc.description.pubStatusPublisheden_US
dc.description.oaCategoryCCen_US
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