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.

翻译：多年来，用户获取金融服务的方式发生了范式转变。随着数字化的进步，越来越多的用户倾向于通过在线方式进行金融活动。这导致了海量金融内容的产生。大多数投资者在做出决策前倾向于阅读这些内容。每个行业都有其特定领域的术语，银行与金融服务也不例外。为了充分理解这些内容，用户需要透彻掌握金融术语。当借助一个术语所属的广义类别进行解释时，理解该术语的基本概念会变得容易。这个广义类别被称为上位词。例如，“债券”是金融术语“替代性债券”的上位词。本文提出一种能够对给定金融术语提取并排序其上位词的系统。该系统利用从DBpedia[4]、Investopedia、金融行业业务本体（FIBO）、招股说明书等多种来源获取的金融文本语料进行训练。我们使用FinBERT[3]和FinISH[1]提取这些术语的嵌入表示，并通过SentenceBERT[54]进行微调。我们采用了一种新颖方法利用FIBO中的层次结构来扩充训练集的负样本。最后，我们将系统性能与现有方法进行基准测试。实验表明，该系统性能优于现有方法且具有可扩展性。