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.
Original language | English |
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Pages (from-to) | 124-126 |
Number of pages | 3 |
Journal | Proceedings - International Conference on Computational Linguistics, COLING |
Volume | 29 |
Issue number | 16 |
Publication status | Published - 2022 |
Externally published | Yes |
Event | 16th Workshop on Graph-Based Methods for Natural Language Processing, TextGraphs 2022, in conjunction with the 29th International Conference on Computational Linguistics, COLING 2022 - Gyeongju, Korea, Republic of Duration: 12 Oct 2022 → 17 Oct 2022 |