Ke Tian
ABSTRACT
This paper describes the method that we submitted to the FinSim-2 task on learning similarities for the financial domain. This task aims to automatically classify the Financial domain terms into the most relevant hypernym (or top-level) concept in an external ontology. This paper shows the result of experiments using the Catboost, Attention-LSTM, BERT, RoBERTa to develop an automatic finance domain classifier via word ontology and embedding. The experiment result demonstrates that each model could be an effective method to tackle the FinSim-2 task, respectively.
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- aiai at the FinSim-2 task: Finance Domain Terms Automatic Classification Via Word Ontology and Embedding
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