# Learning Semantic Text Similarity to rank Hypernyms of Financial Terms

arXiv: https://arxiv.org/abs/2303.13475 · HTML: https://mlsanity.com/paper/2303.13475/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 84 references that are in the map.
- First posted on arXiv 20 Mar 2023.

## Builds on (most cited first, 24 of 24)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [HuggingFace's Transformers: State-of-the-art Natural Language Processing](https://mlsanity.com/paper/1910.03771/) (2019)
- [Universal Sentence Encoder](https://mlsanity.com/paper/1803.11175/) (2018)
- [FinBERT: Financial Sentiment Analysis with Pre-trained Language Models](https://mlsanity.com/paper/1908.10063/) (2019)
- [Efficient Natural Language Response Suggestion for Smart Reply](https://mlsanity.com/paper/1705.00652/) (2017)
- [SemEval 2017 Task 10: ScienceIE - Extracting Keyphrases and Relations from Scientific Publications](https://mlsanity.com/paper/1704.02853/) (2017)
- [EDGAR-CORPUS: Billions of Tokens Make The World Go Round](https://mlsanity.com/paper/2109.14394/) (2021)
- [Improving Hypernymy Detection with an Integrated Path-based and Distributional Method](https://mlsanity.com/paper/1603.06076/) (2016)
- [Hearst Patterns Revisited: Automatic Hypernym Detection from Large Text Corpora](https://mlsanity.com/paper/1806.03191/) (2018)
- [Taxonomy Induction using Hypernym Subsequences](https://mlsanity.com/paper/1704.07626/) (2017)
- [IITK at the FinSim Task: Hypernym Detection in Financial Domain via Context-Free and Contextualized Word Embeddings](https://mlsanity.com/paper/2007.11201/) (2020)
- [INRIASAC: Simple Hypernym Extraction Methods](https://mlsanity.com/paper/1502.01271/) (2015)
- [FinEAS: Financial Embedding Analysis of Sentiment](https://mlsanity.com/paper/2111.00526/) (2021)
- [From syntactic structure to semantic relationship: hypernym extraction from definitions by recurrent neural networks using the part of speech information](https://mlsanity.com/paper/2012.03418/) (2020)
- [FinMatcher at FinSim-2: Hypernym Detection in the Financial Services Domain using Knowledge Graphs](https://mlsanity.com/paper/2103.01576/) (2021)
- [JSI at the FinSim-2 task: Ontology-Augmented Financial Concept Classification](https://mlsanity.com/paper/2106.09230/) (2021)
- [Yseop at FinSim-3 Shared Task 2021: Specializing Financial Domain Learning with Phrase Representations](https://mlsanity.com/paper/2108.09485/) (2021)
- [MIT at SemEval-2017 Task 10: Relation Extraction with Convolutional Neural Networks](https://mlsanity.com/paper/1704.01523/) (2017)
- [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)

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

- [BoostTaxo: Zero-Shot Taxonomy Induction via Boosting-Style Agentic Reasoning and Constraint-Aware Calibration](https://mlsanity.com/paper/2605.12520/) (2026)

## Abstract (arXiv)

Over the years, there has been a paradigm shift in how users access financial services. With the advancement of digitalization more users have been preferring the online mode of performing financial activities. This has led to the generation of a huge volume of financial content. Most investors prefer to go through these contents before making decisions. Every industry has terms that are specific to the domain it operates in. Banking and Financial Services are not an exception to this. In order to fully comprehend these contents, one needs to have a thorough understanding of the financial terms. Getting a basic idea about a term becomes easy when it is explained with the help of the broad category to which it belongs. This broad category is referred to as hypernym. For example, "bond" is a hypernym of the financial term "alternative debenture". In this paper, we propose a system capable of extracting and ranking hypernyms for a given financial term. The system has been trained with financial text corpora obtained from various sources like DBpedia [4], Investopedia, Financial Industry Business Ontology (FIBO), prospectus and so on. Embeddings of these terms have been extracted using FinBERT [3], FinISH [1] and fine-tuned using SentenceBERT [54]. A novel approach has been used to augment the training set with negative samples. It uses the hierarchy present in FIBO. Finally, we benchmark the system performance with that of the existing ones. We establish that it performs better than the existing ones and is also scalable.