MEMD-ABSA: a multi-element multi-domain dataset for aspect-based sentiment analysis.
Aspect-based sentiment analysis is a long-standing research interest in the field of opinion mining, and in recent years, researchers have gradually shifted their focus from simple ABSA subtasks to end-to-end multi-element ABSA tasks. However, the datasets currently used in the research are limited...
| Publicado en: | Language Resources & Evaluation Vol. 59; no. 3; pp. 2501 - 2530 |
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| Autores principales: | , , , , , , , , , , |
| Formato: | Artículo |
| Publicado: |
Springer Nature
Sep2025
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| Materias: | |
| 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=186909079&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 186909079 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: 186909079 10.1007/s10579-025-09820-9 ppf: 2501 ppct: 29 formats: fmt: – @attributes: type: T – @attributes: type: P size: 797KB tig: atl: MEMD-ABSA: a multi-element multi-domain dataset for aspect-based sentiment analysis. aug: au: Cai, Hongjie Song, Nan Wang, Zengzhi Xie, Qiming Zhao, Qiankun Li, Ke Wu, Siwei Liu, Shijie Ma, Heqing Yu, Jianfei Xia, Rui affil: https://ror.org/00xp9wg62 School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China su: Sentiment analysis Generative artificial intelligence sug: subj: Sentiment analysis Generative artificial intelligence keyword: Aspect-based sentiment analysis Implicit expression Natural language processing Opinion mining ab: Aspect-based sentiment analysis is a long-standing research interest in the field of opinion mining, and in recent years, researchers have gradually shifted their focus from simple ABSA subtasks to end-to-end multi-element ABSA tasks. However, the datasets currently used in the research are limited to individual elements of specific tasks, usually focusing on in-domain settings, ignoring implicit aspects and opinions, and with a small data scale. To address these issues, we propose a large-scale Multi-Element Multi-Domain dataset (MEMD) that covers the four elements across five domains, including nearly 20,000 review sentences and 30,000 quadruples annotated with both explicit and implicit aspects and opinions for ABSA research. Meanwhile, we conduct experiments on multiple ABSA subtasks under the open domain setting to verify the effectiveness of several generative and non-generative baselines, and the results show that open domain ABSA as well as mining implicit aspects and opinions remain ongoing challenges to be addressed. 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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