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Hypernym and synonym matching are one of the mainstream Natural LanguageProcessing (NLP) tasks. In this paper, we present systems that attempt to solvethis problem. We designed these systems to participate in the FinSim-3, ashared task of FinNLP workshop at IJCAI-2021. The shared task is focused onsolving this problem for the financial domain. We experimented with varioustransformer based pre-trained embeddings by fine-tuning these for eitherclassification or phrase similarity tasks. We also augmented the provideddataset with abbreviations derived from prospectus provided by the organizersand definitions of the financial terms from DBpedia [Auer et al., 2007],Investopedia, and the Financial Industry Business Ontology (FIBO). Our bestperforming system uses both FinBERT [Araci, 2019] and data augmentation fromthe afore-mentioned sources. We observed that term expansion using dataaugmentation in conjunction with semantic similarity is beneficial for thistask and could be useful for the other tasks that deal with short phrases. Ourbest performing model (Accuracy: 0.917, Rank: 1.156) was developed byfine-tuning SentenceBERT [Reimers et al., 2019] (with FinBERT at the backend)over an extended labelled set created using the hierarchy of labels present inFIBO.

Term Expansion and FinBERT fine-tuning for Hypernym and Synonym Ranking of Financial Terms