Mining Mathematical Documents for Question Answering via Unsupervised Formula Labeling

November 12, 2022 · Entered Twilight · 🏛 ACM/IEEE Joint Conference on Digital Libraries

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Repo contents: .gitignore, CITATION.CFF, Citation_bibtex.bib, Dockerfile, Example Questions, README.md, apicache, app.py, cache.json, demo, dependencies-ppp.sh, download_ntlk.py, evaluation, example_config.json, fonts, getformula.py, getidentifiers.py, getparts.py, identifier_properties.py, latexformlaidentifiers.py, requirements.txt, semanticsearch, static, templates, user-config.py

Authors Philipp Scharpf, Moritz Schubotz, Bela Gipp arXiv ID 2211.06664 Category cs.IR: Information Retrieval Citations 5 Venue ACM/IEEE Joint Conference on Digital Libraries Repository https://github.com/ag-gipp/MathQA ⭐ 18 Last Checked 2 months ago

Abstract

The increasing number of questions on Question Answering (QA) platforms like Math Stack Exchange (MSE) signifies a growing information need to answer math-related questions. However, there is currently very little research on approaches for an open data QA system that retrieves mathematical formulae using their concept names or querying formula identifier relationships from knowledge graphs. In this paper, we aim to bridge the gap by presenting data mining methods and benchmark results to employ Mathematical Entity Linking (MathEL) and Unsupervised Formula Labeling (UFL) for semantic formula search and mathematical question answering (MathQA) on the arXiv preprint repository, Wikipedia, and Wikidata, which is part of the Wikimedia ecosystem of free knowledge. Based on different types of information needs, we evaluate our system in 15 information need modes, assessing over 7,000 query results. Furthermore, we compare its performance to a commercial knowledge-base and calculation-engine (Wolfram Alpha) and search-engine (Google). The open source system is hosted by Wikimedia at https://mathqa.wmflabs.org. A demovideo is available at purl.org/mathqa.