Cited By
View all- Ghosh SChopra ANaskar S(2023)Learning to Rank Hypernyms of Financial Terms Using Semantic Textual SimilaritySN Computer Science10.1007/s42979-023-02134-z4:5Online publication date: 11-Aug-2023
GOAT at the FinSim-2 task: Learning Word Representations of Financial Data with Customized Corpus
Authors: 
Yulong Pei, Qian ZhangAuthors Info & Claims
Pages 307 - 310
Abstract
In this paper, we present our approaches for the FinSim 2021 Shared Task on Learning Semantic Similarities for the Financial Domain. The aim of the FinSim shared task is to automatically classify a given list of terms from the financial domain into the most relevant hypernym (or top-level) concept in an external ontology. Two different word representations have been compared in our study, i.e., customized word2vec provided by the shared task and FinBERT. We first create a customized corpus from the given prospectuses and relevant articles from Investopedia. Then we train the domain-specific word2vec embeddings using the customized data with customized word2vec and FinBERT as the initialized embeddings respectively. Our experimental results demonstrate that these customized word embeddings can effectively improve the classification performance and achieve better results than the direct utilization of the provided word embeddings. The class imbalance issue of the given data is also explored. We empirically study the classification performance by employing several different strategies for imbalanced classification problems. Our system ranks 2nd on both Average Accuracy and Mean Rank metrics.
References
[1]
Dogu Araci. 2019. Finbert: Financial sentiment analysis with pre-trained language models. arXiv preprint arXiv:1908.10063(2019).
[2]
Georgeta Bordea, Els Lefever, and Paul Buitelaar. 2016. Semeval-2016 task 13: Taxonomy extraction evaluation (texeval-2). In Proceedings of the 10th international workshop on semantic evaluation (semeval-2016). 1081–1091.
[3]
Jose Camacho-Collados, Claudio Delli Bovi, Luis Espinosa-Anke, Sergio Oramas, Tommaso Pasini, Enrico Santus, Vered Shwartz, Roberto Navigli, and Horacio Saggion. 2018. SemEval-2018 task 9: Hypernym discovery. In Proceedings of the 12th International Workshop on Semantic Evaluation (SemEval-2018); 2018 Jun 5-6; New Orleans, LA. Stroudsburg (PA): ACL; 2018. p. 712–24. ACL (Association for Computational Linguistics).
[4]
Nitesh V Chawla, Kevin W Bowyer, Lawrence O Hall, and W Philip Kegelmeyer. 2002. SMOTE: synthetic minority over-sampling technique. Journal of artificial intelligence research 16 (2002), 321–357.
[5]
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 4171–4186.
[6]
Ismail El Maarouf, Youness Mansar, Virginie Mouilleron, and Dialekti Valsamou-Stanislawski. 2021. The finsim 2020 shared task: Learning semantic representations for the financial domain. In Proceedings of the Second Workshop on Financial Technology and Natural Language Processing. 81–86.
[7]
Haibo He, Yang Bai, Edwardo A Garcia, and Shutao Li. 2008. ADASYN: Adaptive synthetic sampling approach for imbalanced learning. In 2008 IEEE international joint conference on neural networks (IEEE world congress on computational intelligence). IEEE, 1322–1328.
[8]
Pekka Malo, Ankur Sinha, Pekka Korhonen, Jyrki Wallenius, and Pyry Takala. 2014. Good debt or bad debt: Detecting semantic orientations in economic texts. Journal of the Association for Information Science and Technology 65, 4(2014), 782–796.
[9]
Youness Mansar, Juyeon Kang, and Ismail El Maarouf. 2021. FinSim-2 2021 Shared Task: Learning Semantic Similarities for the Financial Domain. In Proceedings of The Web Conference 2021 (Virtual Edition).
[10]
Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781(2013).
[11]
Yanmin Sun, Andrew KC Wong, and Mohamed S Kamel. 2009. Classification of imbalanced data: A review. International journal of pattern recognition and artificial intelligence 23, 04(2009), 687–719.
[12]
Chengyu Wang and Xiaofeng He. 2020. Birre: learning bidirectional residual relation embeddings for supervised hypernymy detection. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 3630–3640.
[13]
Zheng Yu, Haixun Wang, Xuemin Lin, and Min Wang. 2015. Learning term embeddings for hypernymy identification. In Twenty-Fourth International Joint Conference on Artificial Intelligence.
Recommendations
A Comparison of Pre-Trained Language Models for Multi-Class Text Classification in the Financial Domain
WWW '21: Companion Proceedings of the Web Conference 2021Neural networks for language modeling have been proven effective on several sub-tasks of natural language processing. Training deep language models, however, is time-consuming and computationally intensive. Pre-trained language models such as BERT are ...
The FinSim-2 2021 Shared Task: Learning Semantic Similarities for the Financial Domain
WWW '21: Companion Proceedings of the Web Conference 2021The FinSim-2 is a second edition of FinSim Shared Task on Learning Semantic Similarities for the Financial Domain, colocated with the FinWeb workshop. FinSim-2 proposed the challenge to automatically learn effective and precise semantic models for the ...
PolyU-CBS at the FinSim-2 Task: Combining Distributional, String-Based and Transformers-Based Features for Hypernymy Detection in the Financial Domain
WWW '21: Companion Proceedings of the Web Conference 2021In 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 ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
April 2021
726 pages
ISBN:9781450383134
DOI:10.1145/3442442
Copyright © 2021 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
Sponsors
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 03 June 2021
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
WWW '21
Sponsor:
Acceptance Rates
Overall Acceptance Rate 1,899 of 8,196 submissions, 23%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- View Citations1Total Citations
- 426Total Downloads
- Downloads (Last 12 months)113
- Downloads (Last 6 weeks)19
Reflects downloads up to 03 Mar 2025
Other Metrics
Citations
Cited By
View all- Ghosh SChopra ANaskar S(2023)Learning to Rank Hypernyms of Financial Terms Using Semantic Textual SimilaritySN Computer Science10.1007/s42979-023-02134-z4:5Online publication date: 11-Aug-2023
View Options
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML FormatLogin options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in