@inproceedings{weingart-etal-2024-modelling-argumentation,
title = "Modelling Argumentation for an User Opinion Aggregation Tool",
author = "Weingart, Pablo and
Wambsganss, Thiemo and
Soellner, Matthias",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.1009",
pages = "11548--11559",
abstract = "We introduce an argumentation annotation scheme that models basic argumentative structure and additional contextual details across diverse user opinion domains. Drawing from established argumentation modeling approaches and related theory on user opinions, the scheme integrates the concepts of argumentative components, specificity, sentiment and aspects of the user opinion domain. Our freely available dataset includes 1,016 user opinions with 7,266 sentences, spanning products from 19 e-commerce categories, restaurants, hotels, local services, and mobile applications. Utilizing the dataset, we trained three transformer-based models, demonstrating their efficacy in predicting the annotated classes for identifying argumentative statements and contextual details from user opinion documents. Finally, we evaluate a prototypical dashboard that integrates the model inferences to aggregate information and rank exemplary products based on a vast array of user opinions. Early results from an experimental evaluation with eighteen users include positive user perceptions but also highlight challenges when condensing detailed argumentative information to users.",
}
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%0 Conference Proceedings
%T Modelling Argumentation for an User Opinion Aggregation Tool
%A Weingart, Pablo
%A Wambsganss, Thiemo
%A Soellner, Matthias
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F weingart-etal-2024-modelling-argumentation
%X We introduce an argumentation annotation scheme that models basic argumentative structure and additional contextual details across diverse user opinion domains. Drawing from established argumentation modeling approaches and related theory on user opinions, the scheme integrates the concepts of argumentative components, specificity, sentiment and aspects of the user opinion domain. Our freely available dataset includes 1,016 user opinions with 7,266 sentences, spanning products from 19 e-commerce categories, restaurants, hotels, local services, and mobile applications. Utilizing the dataset, we trained three transformer-based models, demonstrating their efficacy in predicting the annotated classes for identifying argumentative statements and contextual details from user opinion documents. Finally, we evaluate a prototypical dashboard that integrates the model inferences to aggregate information and rank exemplary products based on a vast array of user opinions. Early results from an experimental evaluation with eighteen users include positive user perceptions but also highlight challenges when condensing detailed argumentative information to users.
%U https://aclanthology.org/2024.lrec-main.1009
%P 11548-11559
Markdown (Informal)
[Modelling Argumentation for an User Opinion Aggregation Tool](https://aclanthology.org/2024.lrec-main.1009) (Weingart et al., LREC-COLING 2024)
ACL
- Pablo Weingart, Thiemo Wambsganss, and Matthias Soellner. 2024. Modelling Argumentation for an User Opinion Aggregation Tool. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 11548–11559, Torino, Italia. ELRA and ICCL.