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http://hdl.handle.net/10397/90384
| 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 (https://creativecommons.org/licenses/by/4.0/). 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 https://doi.org/10.1145/3442442.3451387 |
| Appears in Collections: | Conference Paper |
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| File | Description | Size | Format | |
|---|---|---|---|---|
| a0863_n01_FINSIM_Shared_Task.pdf | 439.82 kB | Adobe PDF | View/Open |
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