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AdaGram in Python: An AI Framework for Multi-Sense Embedding in Text and Scientific Formulas

Author

Listed:
  • Arun Josephraj Arokiaraj

    (Department of Basic Sciences, Ernst-Abbe University of Applied Sciences Jena, Carl-Zeiss-Promenade 2, 07745 Jena, Germany)

  • Samah Ibrahim

    (Department of Computer Science, Gulf University for Science and Technology, Hawally 32093, Kuwait)

  • André Then

    (Department of Mathematics and Computer Science, Friedrich Schiller University Jena, Fürstengraben, 07743 Jena, Germany)

  • Bashar Ibrahim

    (Department of Mathematics and Computer Science, Friedrich Schiller University Jena, Fürstengraben, 07743 Jena, Germany
    Department of Mathematics & Natural Sciences and Centre for Applied Mathematics & Bioinformatics, Gulf University for Science and Technology, Hawally 32093, Kuwait
    European Virus Bioinformatics Center, Leutragraben 1, 07743 Jena, Germany)

  • Stephan Peter

    (Department of Basic Sciences, Ernst-Abbe University of Applied Sciences Jena, Carl-Zeiss-Promenade 2, 07745 Jena, Germany)

Abstract

The Adaptive Skip-gram (AdaGram) algorithm extends traditional word embeddings by learning multiple vector representations per word, enabling the capture of contextual meanings and polysemy. Originally implemented in Julia, AdaGram has seen limited adoption due to ecosystem fragmentation and the comparative scarcity of Julia’s machine learning tooling compared to Python’s mature frameworks. In this work, we present a Python-based reimplementation of AdaGram that facilitates broader integration with modern machine learning tools. Our implementation expands the model’s applicability beyond natural language, enabling the analysis of scientific notation—particularly chemical and physical formulas encoded in LaTeX. We detail the algorithmic foundations, preprocessing pipeline, and hyperparameter configurations needed for interdisciplinary corpora. Evaluations on real-world texts and LaTeX-encoded formulas demonstrate AdaGram’s effectiveness in unsupervised word sense disambiguation. Comparative analyses highlight the importance of corpus design and parameter tuning. This implementation opens new applications in formula-aware literature search engines, ambiguity reduction in automated scientific summarization, and cross-disciplinary concept alignment.

Suggested Citation

  • Arun Josephraj Arokiaraj & Samah Ibrahim & André Then & Bashar Ibrahim & Stephan Peter, 2025. "AdaGram in Python: An AI Framework for Multi-Sense Embedding in Text and Scientific Formulas," Mathematics, MDPI, vol. 13(14), pages 1-27, July.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:14:p:2241-:d:1699129
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    References listed on IDEAS

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    1. Xu Yuan & Jiaxi Chen & Yingbo Wang & Anni Chen & Yiou Huang & Wenhong Zhao & Shuo Yu, 2024. "Semantic-Enhanced Knowledge Graph Completion," Mathematics, MDPI, vol. 12(3), pages 1-16, January.
    2. Vahe Tshitoyan & John Dagdelen & Leigh Weston & Alexander Dunn & Ziqin Rong & Olga Kononova & Kristin A. Persson & Gerbrand Ceder & Anubhav Jain, 2019. "Unsupervised word embeddings capture latent knowledge from materials science literature," Nature, Nature, vol. 571(7763), pages 95-98, July.
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