Towards Multilingual LLM-Based Approaches for Automatic Dewey Decimal Classification

The usage of classification systems is a standard method in libraries to organize all kind of materials. The Dewey Decimal Classification System (DDC) is widely used for this task. Even though approaches exist since the 1970s to automate this classification task, it is most often still a time consuming manual process. With the constantly increasing number of publications the need for automation support is growing. Current approaches have certain limitations e.g. only mono- or bi-lingual support, limited accuracy for research domains, limited to higher levels in the DDC hierarchies. The usage of Large Language Models (LLMs) opens new possibilities to support librarians in their work. In this paper we present preliminarily a study to evaluate the usage of BERT to handle a DDC classification task in the linguistic domain. In addition, we analyze the effect of a more condensed representation of full text on the performance of LLMs for this task. Results on multilingual texts are comparable to recent performances on monolingual inputs.