Learning to Rank Hypernyms of Financial Terms Using Semantic Textual Similarity

Over the years, with the advancement of digitalization, investors have started embracing the online mode of performing financial activities. Most investors prefer to read contents over the Internet before making decisions. The financial services’ industry has terms and concepts that are complex and difficult to understand. To fully comprehend these contents, one needs to have a thorough understanding of these terms. Getting a basic idea about a term becomes easy when it is explained with the help of the broad category to which it belongs. This broad category is referred to as hypernym. In this paper, we propose a system capable of extracting and ranking hypernyms for a given financial term. The system has been trained with financial text corpora obtained from various sources. Embeddings of financial terms have been extracted using domain-specific embeddings and fine-tuned using SentenceBERT as reported by Reimers (in: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), Association for Computational Linguistics, Hong Kong, 2019). A novel approach has been used to augment the training set with negative samples. Finally, we benchmark the system performance with that of the existing ones. We establish that it performs better than the existing ones and is also scalable. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.