Please use this identifier to cite or link to this item:
PIRA download icon_1.1View/Download Full Text
Title: PolyU-CBS at the FinSim-2 task : combining distributional, string-based and transformers-based features for hypernymy detection in the financial domain
Authors: Chersoni, E 
Huang, CR 
Issue Date: Apr-2021
Source: WWW '21: Companion Proceedings of the Web Conference 2021, Ljubljana Slovenia, April 2021, p. 316-319
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.
Keywords: Distributional models
Financial NLP
Hypernymy detection
DOI: 10.1145/3442442.3451387
Rights: © 2021 IW3C2 (International World Wide Web Conference Committee), published under Creative Commons CC-BY 4.0 License.
This paper is published under the Creative Commons Attribution 4.0 International (CC-BY 4.0) license ( Authors reserve their rights to disseminate the work on their personal and corporate Web sites with the appropriate attribution.
The following publication Chersoni, E., & Huang, C. R. (2021, April). PolyU-CBS at the FinSim-2 Task: Combining Distributional, String-Based and Transformers-Based Features for Hypernymy Detection in the Financial Domain. In Companion Proceedings of the Web Conference 2021, p. 316-319 is available at
Appears in Collections:Conference Paper

Files in This Item:
File Description SizeFormat 
a0863_n01_FINSIM_Shared_Task.pdf439.82 kBAdobe PDFView/Open
Open Access Information
Status open access
File Version Version of Record
View full-text via PolyU eLinks SFX Query
Show full item record

Page views

Last Week
Last month
Citations as of May 28, 2023


Citations as of May 28, 2023


Citations as of Jun 2, 2023

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.