How to select samples for active learning? Document clustering with active learning methodology

• Published in: Proceedings (International Conference on Engineering of Complex Computer Systems. Online) pp. 42 - 50
• Main Authors: Ropiak, Norbert, Gniewkowski, Mateusz, Swedrowski, Michal, Pogoda, Michal, Gawron, Karol, Bojanowski, Bartlomiej, Walkowiak, Tomasz
• Format: Conference Proceeding
• Language: English
• Published: IEEE 14.06.2023
• Subjects: active learning; Annotations; Dimensionality reduction; document clustering; Labeling; natural language processing; Ontologies; Task analysis; Training
• ISSN: 2770-8535
• DOI: 10.1109/ICECCS59891.2023.00015

In this paper, we investigate the applicability of the Active Learning technique to text clustering and topic modeling tasks. The aforementioned problems are often a non-trivial task due to the ambiguity of text similarity meaning. In our experiments, we implemented the Active Learning approach using automatic annotation from datasets with prepared labels. In a simulated study conducted on Polish and English datasets, we show how labeling a relatively small carefully selected number of examples can improve the quality of clustering relative to approaches based on a general notion of text similarity. We compare a number of techniques for selecting samples for labeling, dimensionality reduction and training approaches in order to compare and obtain the best quality of the resulting clusters with a minimum number of annotations. The obtained results show that with a relatively simple approach it is possible to obtain good quality clusters and thus develop classification ontologies in a data-centric approach.