IITK at the FinSim Task: Hypernym Detection in Financial Domain via Context-Free and Contextualized Word Embeddings
Table 2: Performance on subset 1 (MR = mean rank, ACC = accuracy)
['[BOLD] Embedding', '[BOLD] Dim', '[BOLD] Unsupervised  [BOLD] Cosine Sim.', '[BOLD] Unsupervised  [BOLD] Cosine Sim.', '[BOLD] Unsupervised  [BOLD] L1', '[BOLD] Unsupervised  [BOLD] L1', '[BOLD] Unsupervised  [BOLD] L2', '[BOLD] Unsupervised  [BOLD] L2', '[BOLD] Supervised  [BOLD] Naïve Bayes', '[BOLD] Supervised  [BOLD] Naïve Bayes', '[BOLD] Supervised  [BOLD] Naïve Bayes', '[BOLD] Supervised  [BOLD] Logistic Regression', '[BOLD] Supervised  [BOLD] Logistic Regression', '[BOLD] Supervised  [BOLD] Logistic Regression']
We discuss the performance of all the approaches and systems on the test dataset. It is clear from the table that unsupervised approaches (cosine similarity, L1 norm and L2 norm) prove to be better than supervised approaches (Naïve Bayes and Logistic Regression) for subset 1 with Word2vec word-embeddings. Among the unsupervised, L2 norm dominates. For BERT embeddings, logistic regression dominates.