@inproceedings{kosar-etal-2023-advancing,
title = "Advancing Topical Text Classification: A Novel Distance-Based Method with Contextual Embeddings",
author = "Kosar, Andriy and
De Pauw, Guy and
Daelemans, Walter",
editor = "Mitkov, Ruslan and
Angelova, Galia",
booktitle = "Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing",
month = sep,
year = "2023",
address = "Varna, Bulgaria",
publisher = "INCOMA Ltd., Shoumen, Bulgaria",
url = "https://aclanthology.org/2023.ranlp-1.64",
pages = "586--597",
abstract = "This study introduces a new method for distance-based unsupervised topical text classification using contextual embeddings. The method applies and tailors sentence embeddings for distance-based topical text classification. This is achieved by leveraging the semantic similarity between topic labels and text content, and reinforcing the relationship between them in a shared semantic space. The proposed method outperforms a wide range of existing sentence embeddings on average by 35{\%}. Presenting an alternative to the commonly used transformer-based zero-shot general-purpose classifiers for multiclass text classification, the method demonstrates significant advantages in terms of computational efficiency and flexibility, while maintaining comparable or improved classification results.",
}
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%0 Conference Proceedings
%T Advancing Topical Text Classification: A Novel Distance-Based Method with Contextual Embeddings
%A Kosar, Andriy
%A De Pauw, Guy
%A Daelemans, Walter
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing
%D 2023
%8 September
%I INCOMA Ltd., Shoumen, Bulgaria
%C Varna, Bulgaria
%F kosar-etal-2023-advancing
%X This study introduces a new method for distance-based unsupervised topical text classification using contextual embeddings. The method applies and tailors sentence embeddings for distance-based topical text classification. This is achieved by leveraging the semantic similarity between topic labels and text content, and reinforcing the relationship between them in a shared semantic space. The proposed method outperforms a wide range of existing sentence embeddings on average by 35%. Presenting an alternative to the commonly used transformer-based zero-shot general-purpose classifiers for multiclass text classification, the method demonstrates significant advantages in terms of computational efficiency and flexibility, while maintaining comparable or improved classification results.
%U https://aclanthology.org/2023.ranlp-1.64
%P 586-597
Markdown (Informal)
[Advancing Topical Text Classification: A Novel Distance-Based Method with Contextual Embeddings](https://aclanthology.org/2023.ranlp-1.64) (Kosar et al., RANLP 2023)
ACL