Problem-solving Recognition in Scientific Text

Kevin Heffernan, Simone Teufel


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.
Anthology ID:
2022.lrec-1.650
Volume:
Proceedings of the Thirteenth Language Resources and Evaluation Conference
Month:
June
Year:
2022
Address:
Marseille, France
Editors:
Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Jan Odijk, Stelios Piperidis
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
6045–6058
Language:
URL:
https://aclanthology.org/2022.lrec-1.650
DOI:
Bibkey:
Cite (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.
Cite (Informal):
Problem-solving Recognition in Scientific Text (Heffernan & Teufel, LREC 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.lrec-1.650.pdf