Enhancing Word Embeddings for Improved Semantic Alignment

Julian Szymański; Maksymilian Operlejn; Paweł Weichbroth

doi:10.3390/app142411519

Enhancing Word Embeddings for Improved Semantic Alignment

Abstract

This study introduces a method for the improvement of word vectors, addressing the limitations of traditional approaches like Word2Vec or GloVe through introducing into embeddings richer semantic properties. Our approach leverages supervised learning methods, with shifts in vectors in the representation space enhancing the quality of word embeddings. This ensures better alignment with semantic reference resources, such as WordNet. The effectiveness of the method has been demonstrated through the application of modified embeddings to text classification and clustering. We also show how our method influences document class distributions, visualized through PCA projections. By comparing our results with state-of-the-art approaches and achieving better accuracy, we confirm the effectiveness of the proposed method. The results underscore the potential of adaptive embeddings to improve both the accuracy and efficiency of semantic analysis across a range of NLP.

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Category:

Articles

Type:

artykuły w czasopismach

Published in:

Applied Sciences-Basel no. 14,
ISSN: 2076-3417

Language:

English

Publication year:

2024

Bibliographic description:

Szymański J., Operlejn M., Weichbroth P.: Enhancing Word Embeddings for Improved Semantic Alignment// Applied Sciences-Basel -,iss. 14/24 (2024), s.11519-

DOI: