PolyU-CBS at the FinSim-2 Task: Combining Distributional, String-Based and Transformers-Based Features for Hypernymy Detection in the Financial Domain

Abstract

In this contribution, we describe the systems presented by the PolyU CBS Team at the second Shared Task on Learning Semantic Similarities for the Financial Domain (FinSim-2), where participating teams had to identify the right hypernyms for a list of target terms from the financial domain. For this task, we ran our classification experiments with several distributional, string-based, and Transformer features. Our results show that a simple logistic regression classifier, when trained on a combination of word embeddings, semantic and string similarity metrics and BERT-derived probabilities, achieves a strong performance (above 90%) in financial hypernymy detection.