no code implementations • EMNLP (sdp) 2020 • Takuto Asakura, André Greiner-Petter, Akiko Aizawa, Yusuke Miyao
Our results indicate that it is worthwhile to grow the techniques for the proposed task to contribute to the further progress of mathematical language processing.
no code implementations • 27 Feb 2024 • Tomáš Horych, Martin Wessel, Jan Philip Wahle, Terry Ruas, Jerome Waßmuth, André Greiner-Petter, Akiko Aizawa, Bela Gipp, Timo Spinde
MAGPIE confirms that MTL is a promising approach for addressing media bias detection, enhancing the accuracy and efficiency of existing models.
1 code implementation • 22 May 2023 • Ankit Satpute, André Greiner-Petter, Moritz Schubotz, Norman Meuschke, Akiko Aizawa, Olaf Teschke, Bela Gipp
This demo paper presents the first tool to annotate the reuse of text, images, and mathematical formulae in a document pair -- TEIMMA.
no code implementations • 12 May 2023 • Bela Gipp, André Greiner-Petter, Moritz Schubotz, Norman Meuschke
This project investigated new approaches and technologies to enhance the accessibility of mathematical content and its semantic information for a broad range of information retrieval applications.
no code implementations • 17 Sep 2021 • Howard S. Cohl, Moritz Schubotz, Abdou Youssef, André Greiner-Petter, Jürgen Gerhard, Bonita V. Saunders, Marjorie A. ~McClain
Using LaTeX, LaTeXML, and tools generated for use in the National Institute of Standards (NIST) Digital Library of Mathematical Functions, semantically enhanced mathematical LaTeX markup (semantic LaTeX) is achieved by using a semantic macro set.
no code implementations • 30 Nov 2020 • André Greiner-Petter
In mathematics, LaTeX is the de facto standard to prepare documents, e. g., scientific publications.
no code implementations • 20 Mar 2020 • Moritz Schubotz, André Greiner-Petter, Norman Meuschke, Olaf Teschke, Bela Gipp
This poster summarizes our contributions to Wikimedia's processing pipeline for mathematical formulae.
no code implementations • 20 May 2019 • André Greiner-Petter, Terry Ruas, Moritz Schubotz, Akiko Aizawa, William Grosky, Bela Gipp
Nowadays, Machine Learning (ML) is seen as the universal solution to improve the effectiveness of information retrieval (IR) methods.