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.
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.
Original language | English |
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Title of host publication | WWW '21: Companion Proceedings of the Web Conference 2021 |
Editors | Jure Leskovec, Marko Grobelnik, Marc Najork, Jie Tang, Leila Zia |
Publisher | Association for Computing Machinery (ACM) |
Pages | 316-319 |
ISBN (Electronic) | 978-1-4503-8313-4 |
DOIs | |
Publication status | Published - 3 Jun 2021 |
Event | The First Workshop on Financial Technology on the Web - Online Duration: 14 Apr 2021 → 15 Apr 2021 |
Conference
Conference | The First Workshop on Financial Technology on the Web |
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Abbreviated title | FinWeb |
Period | 14/04/21 → 15/04/21 |