MathGloss: Building mathematical glossaries from text
- URL: http://arxiv.org/abs/2311.12649v1
- Date: Tue, 21 Nov 2023 14:49:00 GMT
- Title: MathGloss: Building mathematical glossaries from text
- Authors: Lucy Horowitz, Valeria de Paiva
- Abstract summary: MathGloss is a database of undergraduate concepts in mathematics.
It uses modern natural language processing (NLP) tools and resources already available on the web.
- Score: 0.620048328543366
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: MathGloss is a project to create a knowledge graph (KG) for undergraduate
mathematics from text, automatically, using modern natural language processing
(NLP) tools and resources already available on the web. MathGloss is a linked
database of undergraduate concepts in mathematics. So far, it combines five
resources: (i) Wikidata, a collaboratively edited, multilingual knowledge graph
hosted by the Wikimedia Foundation, (ii) terms covered in mathematics courses
at the University of Chicago, (iii) the syllabus of the French undergraduate
mathematics curriculum which includes hyperlinks to the automated theorem
prover Lean 4, (iv) MuLiMa, a multilingual dictionary of mathematics curated by
mathematicians, and (v) the nLab, a wiki for category theory also curated by
mathematicians. MathGloss's goal is to bring together resources for learning
mathematics and to allow every mathematician to tailor their learning to their
own preferences. Moreover, by organizing different resources for learning
undergraduate mathematics alongside those for learning formal mathematics, we
hope to make it easier for mathematicians and formal tools (theorem provers,
computer algebra systems, etc) experts to "understand" each other and break
down some of the barriers to formal math.
Related papers
- FrontierMath: A Benchmark for Evaluating Advanced Mathematical Reasoning in AI [2.1061205911958876]
FrontierMath is a benchmark of hundreds of original, exceptionally challenging mathematics problems crafted and vetted by expert mathematicians.
Current state-of-the-art AI models solve under 2% of problems, revealing a vast gap between AI capabilities and the prowess of the mathematical community.
As AI systems advance toward expert-level mathematical abilities, FrontierMath offers a rigorous testbed that quantifies their progress.
arXiv Detail & Related papers (2024-11-07T17:07:35Z) - MathBench: Evaluating the Theory and Application Proficiency of LLMs with a Hierarchical Mathematics Benchmark [82.64129627675123]
MathBench is a new benchmark that rigorously assesses the mathematical capabilities of large language models.
MathBench spans a wide range of mathematical disciplines, offering a detailed evaluation of both theoretical understanding and practical problem-solving skills.
arXiv Detail & Related papers (2024-05-20T17:52:29Z) - A Semantic Search Engine for Mathlib4 [3.4826238218770813]
We present a semantic search engine for mathlib4 that accepts informal queries and finds the relevant theorems.
We also establish a benchmark for assessing the performance of various search engines for mathlib4.
arXiv Detail & Related papers (2024-03-20T05:23:09Z) - Machine learning and information theory concepts towards an AI
Mathematician [77.63761356203105]
The current state-of-the-art in artificial intelligence is impressive, especially in terms of mastery of language, but not so much in terms of mathematical reasoning.
This essay builds on the idea that current deep learning mostly succeeds at system 1 abilities.
It takes an information-theoretical posture to ask questions about what constitutes an interesting mathematical statement.
arXiv Detail & Related papers (2024-03-07T15:12:06Z) - MathScale: Scaling Instruction Tuning for Mathematical Reasoning [70.89605383298331]
Large language models (LLMs) have demonstrated remarkable capabilities in problem-solving.
However, their proficiency in solving mathematical problems remains inadequate.
We propose MathScale, a simple and scalable method to create high-quality mathematical reasoning data.
arXiv Detail & Related papers (2024-03-05T11:42:59Z) - OntoMath${}^{\mathbf{PRO}}$ 2.0 Ontology: Updates of the Formal Model [68.8204255655161]
The main attention is paid to the development of a formal model for the representation of mathematical statements in the Open Linked Data cloud.
The proposed model is intended for applications that extract mathematical facts from natural language mathematical texts and represent these facts as Linked Open Data.
The model is used in development of a new version of the OntoMath$mathrmPRO$ ontology of professional mathematics is described.
arXiv Detail & Related papers (2023-03-17T20:29:17Z) - Mathematical Capabilities of ChatGPT [35.71603158908465]
We release two new datasets: GHOSTS and miniGHOSTS.
These are the first natural-language datasets curated by working researchers in mathematics.
We benchmark the models on a range of fine-grained performance metrics.
arXiv Detail & Related papers (2023-01-31T18:59:03Z) - JiuZhang: A Chinese Pre-trained Language Model for Mathematical Problem
Understanding [74.12405417718054]
This paper aims to advance the mathematical intelligence of machines by presenting the first Chinese mathematical pre-trained language model(PLM)
Unlike other standard NLP tasks, mathematical texts are difficult to understand, since they involve mathematical terminology, symbols and formulas in the problem statement.
We design a novel curriculum pre-training approach for improving the learning of mathematical PLMs, consisting of both basic and advanced courses.
arXiv Detail & Related papers (2022-06-13T17:03:52Z) - Art Speaks Maths, Maths Speaks Art [53.473846742702854]
Our interdisciplinary team Mathematics for Applications in Cultural Heritage (MACH) aims to use mathematical research for the benefit of the arts and humanities.
Our ultimate goal is to create user-friendly software toolkits for artists, art conservators and archaeologists.
arXiv Detail & Related papers (2020-07-17T10:24:23Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.