DICoE@FinSim-3: Financial Hypernym Detection using Augmented Terms and Distance-based Features
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Abstract (arXiv)
We present the submission of team DICoE for FinSim-3, the 3rd Shared Task on Learning Semantic Similarities for the Financial Domain. The task provides a set of terms in the financial domain and requires to classify them into the most relevant hypernym from a financial ontology. After augmenting the terms with their Investopedia definitions, our system employs a Logistic Regression classifier over financial word embeddings and a mix of hand-crafted and distance-based features. Also, for the first time in this task, we employ different replacement methods for out-of-vocabulary terms, leading to improved performance. Finally, we have also experimented with word representations generated from various financial corpora. Our best-performing submission ranked 4th on the task's leaderboard.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 5 of its 17 references that are in the map. First posted on arXiv 30 Sep 2021.
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References · 5
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- 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,346
- 2020FinBERT: A Pretrained Language Model for Financial Communicationscited by 161
- 2020IITK at the FinSim Task: Hypernym Detection in Financial Domain via Context-Free and Contextualized Word Embeddingscited by 4
- 2018Magnitude: A Fast, Efficient Universal Vector Embedding Utility Packagecited by 3
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