From Textbooks to Knowledge: A Case Study in Harvesting Axiomatic Knowledge from Textbooks to Solve Geometry Problems

By Mrinmaya Sachan, Kumar Avinava Dubey, Eric P. Xing. 2017

48 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Computer Science Applications, Natural Language Processing, Information Extraction, Code Generation & Program Synthesis

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Citations in the corpus

Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering (2022, background): “Different from existing datasets [50, 17, 52, 31, 24], SCIENCEQA covers diverse topics across three subjects: natural science, social science, and language science.”
Large Language Models for Mathematical Reasoning: Progresses and Challenges (2024, unknown): “An example can be seen as follows and Table 3 (Seo et al., 2015) 186 G EO S++ (Sachan et al., 2017) 1.4K G EO S-OS (Sachan and Xing, 2017) 2.2K G EOMETRY 3K (Lu et al., 2021) 3K G EO QA (Chen et al., 2021a) 5K U NI G EO (Chen et al., 2022) 14.5K”
A Survey of Deep Learning for Mathematical Reasoning (2022, unknown): “Some early datasets are proposed to facilitate research in this domain (Seo et al., 2015; Alvin et al., 2017; Sachan et al., 2017; Sachan and Xing, 2017).”
GeoQA: A Geometric Question Answering Benchmark Towards Multimodal Numerical Reasoning (2021, methodology): “Compared with the existing dataset GeoS and GeoS++ (Seo et al., 2015a; Sachan et al., 2017), our GeoQA is larger, more diverse, provides additional program annotation, thus serves as a promising benchmark to improve both generalization and interpretability of the multimodal numerical reasoning…”
The Gap of Semantic Parsing: A Survey on Automatic Math Word Problem Solvers (2018, methodology): “A subsequent improver of GEOS is presented in [95].”
Inter-GPS: Interpretable Geometry Problem Solving with Formal Language and Symbolic Reasoning (2021, methodology): “Unlike existing methods (Seo et al., 2015; Sachan et al., 2017; Alvin et al., 2017; Sachan et al., 2020), Inter-GPS achieves the explicit symbolic reasoning”
IconQA: A New Benchmark for Abstract Diagram Understanding and Visual Language Reasoning (2021, background): “Some more works have proposed datasets of middle school math or science problems in more practical and complex scenarios [46, 27, 43, 44, 37].”