E2EET: From Pipeline to End-to-end Entity Typing via Transformer-Based Embeddings
Table 3: The results of E2EET model on the modified Wiki, Ontonotes and BBN datasets using various embedding techniques. Misleading metrics are in grey.
['[BOLD] Embeddings', '[BOLD] M-Wiki Acc', '[BOLD] M-Wiki Ma-F1', '[BOLD] M-Wiki Mi-F1', '[BOLD] M-Ontonotes Acc', '[BOLD] M-Ontonotes Ma-F1', '[BOLD] M-Ontonotes Mi-F1', '[BOLD] M-BBN Acc', '[BOLD] M-BBN Ma-F1', '[BOLD] M-BBN Mi-F1']
5.2.1 Baseline performance To evaluate E2EET we test the model after its embedding layer has been initialised with four different embedding techniques. Here, the strict accuracy, macro, and micro F1 scores are calculated with respect to the model’s predictions of every single token in the corpus.