Andriy Kosar


2024

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Advancing CSR Theme and Topic Classification: LLMs and Training Enhancement Insights
Jens Van Nooten | Andriy Kosar
Proceedings of the Joint Workshop of the 7th Financial Technology and Natural Language Processing, the 5th Knowledge Discovery from Unstructured Data in Financial Services, and the 4th Workshop on Economics and Natural Language Processing

In this paper, we present our results of the classification of Corporate Social Responsibility (CSR) Themes and Topics shared task, which encompasses cross-lingual multi-class classification and monolingual multi-label classification. We examine the performance of multiple machine learning (ML) models, ranging from classical models to pre-trained large language models (LLMs), and assess the effectiveness of Data Augmentation (DA), Data Translation (DT), and Contrastive Learning (CL). We find that state-of-the-art generative LLMs in a zero-shot setup still fall behind on more complex classification tasks compared to fine-tuning local models with enhanced datasets and additional training objectives. Our work provides a wide array of comparisons and highlights the relevance of utilizing smaller language models for more complex classification tasks.

2023

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Advancing Topical Text Classification: A Novel Distance-Based Method with Contextual Embeddings
Andriy Kosar | Guy De Pauw | Walter Daelemans
Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing

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.