# DICoE@FinSim-3: Financial Hypernym Detection using Augmented Terms and Distance-based Features

arXiv: https://arxiv.org/abs/2109.14906 · HTML: https://mlsanity.com/paper/2109.14906/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 17 references that are in the map.
- First posted on arXiv 30 Sep 2021.

## Builds on (most cited first, 5 of 5)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [FinBERT: A Pretrained Language Model for Financial Communications](https://mlsanity.com/paper/2006.08097/) (2020)
- [IITK at the FinSim Task: Hypernym Detection in Financial Domain via Context-Free and Contextualized Word Embeddings](https://mlsanity.com/paper/2007.11201/) (2020)
- [Magnitude: A Fast, Efficient Universal Vector Embedding Utility Package](https://mlsanity.com/paper/1810.11190/) (2018)

## Built on by (most cited first, 1 of 1)

- [Learning Semantic Text Similarity to rank Hypernyms of Financial Terms](https://mlsanity.com/paper/2303.13475/) (2023)

## 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.