Abstract
Machine Learning (ML) and Natural Language Processing (NLP) have started to be applied to math language processing and math knowledge discovery. To fully utilize ML in those areas, there is a pressing need for Math labeled datasets. This paper presents a new dataset that we have derived from the widely used Digital Library of Mathematical Functions (DLMF) of NIST. The dataset is structured and labeled in a specific way. For each math equation and expression in the DLMF, there is a record that provides annotational and contextual elements. An accompanying dataset is also generated from the DLMF. It consists of “Simple XML” files, each organized as marked-up sentences within a marked-up hierarchy of paragraphs/subsections/sections. The math in each sentence is marked up in a way that enables users to extract the actual context of math elements, at various levels of granularity, for contextualized processing. This context-rich, sentence-oriented, equation/expression-centered, symbol-labeled dataset is motivated by the fact that much of ML-based NLP algorithms are sentence oriented.
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Notes
- 1.
For now, the dataset is at https://github.com/abdouyoussef/math-dlmf-dataset/.
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Youssef, A., Miller, B.R. (2020). A Contextual and Labeled Math-Dataset Derived from NIST’s DLMF. In: Benzmüller, C., Miller, B. (eds) Intelligent Computer Mathematics. CICM 2020. Lecture Notes in Computer Science(), vol 12236. Springer, Cham. https://doi.org/10.1007/978-3-030-53518-6_25
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DOI: https://doi.org/10.1007/978-3-030-53518-6_25
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