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

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4 Citations (Scopus)

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.
Original languageEnglish
Title of host publicationWWW '21: Companion Proceedings of the Web Conference 2021
EditorsJure Leskovec, Marko Grobelnik, Marc Najork, Jie Tang, Leila Zia
PublisherAssociation for Computing Machinery (ACM)
Pages316-319
ISBN (Electronic)978-1-4503-8313-4
DOIs
Publication statusPublished - 3 Jun 2021
EventThe First Workshop on Financial Technology on the Web - Online
Duration: 14 Apr 202115 Apr 2021

Conference

ConferenceThe First Workshop on Financial Technology on the Web
Abbreviated titleFinWeb
Period14/04/2115/04/21

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