Maksymilian Operlejn
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Maksymilian Operlejn has a total of 2 co-authors. Here are the top co-authors by number of shared publications:
- Julian Szymański
Gdansk University of Technology
1 shared publication - Paweł Weichbroth
Gdansk University of Technology
1 shared publication
Latest Publications
- Open Access
- 10 December 2024
- Published by MDPI AG in Applied Sciences
- Vol. 14 (24) , 11519
Enhancing Word Embeddings for Improved Semantic Alignment
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.
- 10 December 2024
- Published by MDPI AG in Applied Sciences
- Vol. 14 (24) , 11519
Open AccessEnhancing Word Embeddings for Improved Semantic Alignment
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.