Premise Selection in Natural Language Mathematical Texts
Deborah
Ferreira
author
André
Freitas
author
2020-jul
text
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Association for Computational Linguistics
Online
conference publication
The discovery of supporting evidence for addressing complex mathematical problems is a semantically challenging task, which is still unexplored in the field of natural language processing for mathematical text. The natural language premise selection task consists in using conjectures written in both natural language and mathematical formulae to recommend premises that most likely will be useful to prove a particular statement. We propose an approach to solve this task as a link prediction problem, using Deep Convolutional Graph Neural Networks. This paper also analyses how different baselines perform in this task and shows that a graph structure can provide higher F1-score, especially when considering multi-hop premise selection.
ferreira-freitas-2020-premise
10.18653/v1/2020.acl-main.657
https://www.aclweb.org/anthology/2020.acl-main.657
2020-jul
7365
7374