Abstract
Extraction of supportive premises for a mathematical problem can contribute to profound success in improving automatic reasoning systems. One bottleneck in automated theorem proving is the lack of a proper semantic information retrieval system for mathematical texts. In this paper, we show the effect of keyword extraction in the natural language premise selection (NLPS) shared task proposed in TextGraph-16 that seeks to select the most relevant sentences supporting a given mathematical statement.- Anthology ID:
- 2022.textgraphs-1.14
- Volume:
- Proceedings of TextGraphs-16: Graph-based Methods for Natural Language Processing
- Month:
- October
- Year:
- 2022
- Address:
- Gyeongju, Republic of Korea
- Editors:
- Dmitry Ustalov, Yanjun Gao, Alexander Panchenko, Marco Valentino, Mokanarangan Thayaparan, Thien Huu Nguyen, Gerald Penn, Arti Ramesh, Abhik Jana
- Venue:
- TextGraphs
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 124–126
- Language:
- URL:
- https://aclanthology.org/2022.textgraphs-1.14
- DOI:
- Cite (ACL):
- Doratossadat Dastgheib and Ehsaneddin Asgari. 2022. Keyword-based Natural Language Premise Selection for an Automatic Mathematical Statement Proving. In Proceedings of TextGraphs-16: Graph-based Methods for Natural Language Processing, pages 124–126, Gyeongju, Republic of Korea. Association for Computational Linguistics.
- Cite (Informal):
- Keyword-based Natural Language Premise Selection for an Automatic Mathematical Statement Proving (Dastgheib & Asgari, TextGraphs 2022)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2022.textgraphs-1.14.pdf