Topic Classification

Topic classification aims to automatically assign text documents to predefined categories or topics, facilitating efficient information retrieval and analysis. Current research emphasizes improving model accuracy and efficiency, particularly for low-resource languages and scenarios with limited labeled data, often leveraging pre-trained language models (like BERT and its variants) within neural network architectures or employing clustering techniques combined with TF-IDF methods. These advancements are crucial for various applications, including social science research, news analysis, and metadata enrichment, enabling large-scale automated processing of textual data across diverse domains and languages.