IITK at the FinSim Task: Hypernym Detection in Financial Domain via Context-Free and Contextualized Word Embeddings
Table 4: Results of different systems on the whole test data
['[BOLD] System', '[BOLD] Mean Rank', '[BOLD] Accuracy']
System 1 and 2 show the performance of Word2vec word-embeddings of dimensions 100 and 300, respectively. These systems are a combination of unsupervised and supervised approaches separately applied on subset 1 and 2, respectively. They outperform any of the approaches applied to the aggregate test data. For both the systems, the classification layers consist of L2 norm for subset 1 and Naïve Bayes classifier for subset 2 as they dominate in their respective categories. System 3, reveals the performance of BERT word-embeddings in the embedding layer. It uses the logistic regression classifier, for both subsets 1 and 2, in the classification layer as it performs the best with BERT word-embeddings.