E2EET: From Pipeline to End-to-end Entity Typing via Transformer-Based Embeddings
Table 4: A comparison between our mention-level (ML) model (with three different attention mechanisms), and E2EET on the modified Wiki, Ontonotes and BBN datasets. Top scores (prior to rounding) are in bold.
['[BOLD] Model', '[BOLD] Attention', '[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']
when trained and evaluated upon the modified datasets. Here, E2EET is using BERT embeddings and is evaluated using the same F1 calculations as the mention-level model, i.e. the scores are only calculated across entities as opposed to across all tokens. This means that any non-entities that are incorrectly labelled as entities by E2EET are ignored.