# IITK at the FinSim Task: Hypernym Detection in Financial Domain via Context-Free and Contextualized Word Embeddings

arXiv: https://arxiv.org/abs/2007.11201 · HTML: https://mlsanity.com/paper/2007.11201/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jul 2020.

## Built on by (most cited first, 4 of 4)

- [Exploiting Network Structures to Improve Semantic Representation for the Financial Domain](https://mlsanity.com/paper/2107.05885/) (2021)
- [Term Expansion and FinBERT fine-tuning for Hypernym and Synonym Ranking of Financial Terms](https://mlsanity.com/paper/2107.13764/) (2021)
- [DICoE@FinSim-3: Financial Hypernym Detection using Augmented Terms and Distance-based Features](https://mlsanity.com/paper/2109.14906/) (2021)
- [Learning Semantic Text Similarity to rank Hypernyms of Financial Terms](https://mlsanity.com/paper/2303.13475/) (2023)

## Abstract (arXiv)

In this paper, we present our approaches for the FinSim 2020 shared task on "Learning Semantic Representations for the Financial Domain". The goal of this task is to classify financial terms into the most relevant hypernym (or top-level) concept in an external ontology. We leverage both context-dependent and context-independent word embeddings in our analysis. Our systems deploy Word2vec embeddings trained from scratch on the corpus (Financial Prospectus in English) along with pre-trained BERT embeddings. We divide the test dataset into two subsets based on a domain rule. For one subset, we use unsupervised distance measures to classify the term. For the second subset, we use simple supervised classifiers like Naive Bayes, on top of the embeddings, to arrive at a final prediction. Finally, we combine both the results. Our system ranks 1st based on both the metrics, i.e., mean rank and accuracy.