@inproceedings{heffernan-teufel-2022-problem,
title = "Problem-solving Recognition in Scientific Text",
author = "Heffernan, Kevin and
Teufel, Simone",
booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
month = jun,
year = "2022",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2022.lrec-1.650",
pages = "6045--6058",
abstract = "As far back as Aristotle, problems and solutions have been recognised as a core pattern of thought, and in particular of the scientific method. In this work, we present the novel task of problem-solving recognition in scientific text. Previous work on problem-solving either is not computational, is not adapted to scientific text, or has been narrow in scope. This work provides a new annotation scheme of problem-solving tailored to the scientific domain. We validate the scheme with an annotation study, and model the task using state-of-the-art baselines such as a Neural Relational Topic Model. The agreement study indicates that our annotation is reliable, and results from modelling show that problem-solving expressions in text can be recognised to a high degree of accuracy.",
}
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<abstract>As far back as Aristotle, problems and solutions have been recognised as a core pattern of thought, and in particular of the scientific method. In this work, we present the novel task of problem-solving recognition in scientific text. Previous work on problem-solving either is not computational, is not adapted to scientific text, or has been narrow in scope. This work provides a new annotation scheme of problem-solving tailored to the scientific domain. We validate the scheme with an annotation study, and model the task using state-of-the-art baselines such as a Neural Relational Topic Model. The agreement study indicates that our annotation is reliable, and results from modelling show that problem-solving expressions in text can be recognised to a high degree of accuracy.</abstract>
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%0 Conference Proceedings
%T Problem-solving Recognition in Scientific Text
%A Heffernan, Kevin
%A Teufel, Simone
%S Proceedings of the Thirteenth Language Resources and Evaluation Conference
%D 2022
%8 June
%I European Language Resources Association
%C Marseille, France
%F heffernan-teufel-2022-problem
%X As far back as Aristotle, problems and solutions have been recognised as a core pattern of thought, and in particular of the scientific method. In this work, we present the novel task of problem-solving recognition in scientific text. Previous work on problem-solving either is not computational, is not adapted to scientific text, or has been narrow in scope. This work provides a new annotation scheme of problem-solving tailored to the scientific domain. We validate the scheme with an annotation study, and model the task using state-of-the-art baselines such as a Neural Relational Topic Model. The agreement study indicates that our annotation is reliable, and results from modelling show that problem-solving expressions in text can be recognised to a high degree of accuracy.
%U https://aclanthology.org/2022.lrec-1.650
%P 6045-6058
Markdown (Informal)
[Problem-solving Recognition in Scientific Text](https://aclanthology.org/2022.lrec-1.650) (Heffernan & Teufel, LREC 2022)
ACL
- Kevin Heffernan and Simone Teufel. 2022. Problem-solving Recognition in Scientific Text. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 6045–6058, Marseille, France. European Language Resources Association.