AdaGram in Python: An AI Framework for Multi-Sense Embedding in Text and Scientific Formulas
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 com...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/13/14/2241 |
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| author | Arun Josephraj Arokiaraj Samah Ibrahim André Then Bashar Ibrahim Stephan Peter |
| author_facet | Arun Josephraj Arokiaraj Samah Ibrahim André Then Bashar Ibrahim Stephan Peter |
| author_sort | Arun Josephraj Arokiaraj |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-56c5c1e28a524dbeb83ae9dfdd444ced |
| institution | Kabale University |
| issn | 2227-7390 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-56c5c1e28a524dbeb83ae9dfdd444ced2025-08-20T03:32:27ZengMDPI AGMathematics2227-73902025-07-011314224110.3390/math13142241AdaGram in Python: An AI Framework for Multi-Sense Embedding in Text and Scientific FormulasArun Josephraj Arokiaraj0Samah Ibrahim1André Then2Bashar Ibrahim3Stephan Peter4Department of Basic Sciences, Ernst-Abbe University of Applied Sciences Jena, Carl-Zeiss-Promenade 2, 07745 Jena, GermanyDepartment of Computer Science, Gulf University for Science and Technology, Hawally 32093, KuwaitDepartment of Mathematics and Computer Science, Friedrich Schiller University Jena, Fürstengraben, 07743 Jena, GermanyDepartment of Mathematics and Computer Science, Friedrich Schiller University Jena, Fürstengraben, 07743 Jena, GermanyDepartment of Basic Sciences, Ernst-Abbe University of Applied Sciences Jena, Carl-Zeiss-Promenade 2, 07745 Jena, GermanyThe 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.https://www.mdpi.com/2227-7390/13/14/2241AdaGramword sense disambiguationscientific formula analysissemantic relationshipsnatural language processinginformation retrieval |
| spellingShingle | Arun Josephraj Arokiaraj Samah Ibrahim André Then Bashar Ibrahim Stephan Peter AdaGram in Python: An AI Framework for Multi-Sense Embedding in Text and Scientific Formulas Mathematics AdaGram word sense disambiguation scientific formula analysis semantic relationships natural language processing information retrieval |
| title | AdaGram in Python: An AI Framework for Multi-Sense Embedding in Text and Scientific Formulas |
| title_full | AdaGram in Python: An AI Framework for Multi-Sense Embedding in Text and Scientific Formulas |
| title_fullStr | AdaGram in Python: An AI Framework for Multi-Sense Embedding in Text and Scientific Formulas |
| title_full_unstemmed | AdaGram in Python: An AI Framework for Multi-Sense Embedding in Text and Scientific Formulas |
| title_short | AdaGram in Python: An AI Framework for Multi-Sense Embedding in Text and Scientific Formulas |
| title_sort | adagram in python an ai framework for multi sense embedding in text and scientific formulas |
| topic | AdaGram word sense disambiguation scientific formula analysis semantic relationships natural language processing information retrieval |
| url | https://www.mdpi.com/2227-7390/13/14/2241 |
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