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