Abstract
Textbooks are rich sources of information. Harvesting structured knowledge from textbooks is a key challenge in many educational applications. As a case study, we present an approach for harvesting structured axiomatic knowledge from math textbooks. Our approach uses rich contextual and typographical features extracted from raw textbooks. It leverages the redundancy and shared ordering across multiple textbooks to further refine the harvested axioms. These axioms are then parsed into rules that are used to improve the state-of-the-art in solving geometry problems.- Anthology ID:
- D17-1081
- Volume:
- Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
- Month:
- September
- Year:
- 2017
- Address:
- Copenhagen, Denmark
- Editors:
- Martha Palmer, Rebecca Hwa, Sebastian Riedel
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 773–784
- Language:
- URL:
- https://aclanthology.org/D17-1081
- DOI:
- 10.18653/v1/D17-1081
- Cite (ACL):
- Mrinmaya Sachan, Kumar Dubey, and Eric Xing. 2017. From Textbooks to Knowledge: A Case Study in Harvesting Axiomatic Knowledge from Textbooks to Solve Geometry Problems. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 773–784, Copenhagen, Denmark. Association for Computational Linguistics.
- Cite (Informal):
- From Textbooks to Knowledge: A Case Study in Harvesting Axiomatic Knowledge from Textbooks to Solve Geometry Problems (Sachan et al., EMNLP 2017)
- PDF:
- https://preview.aclanthology.org/corrections-2024-07/D17-1081.pdf
- Data
- GeoS