Named entity recognition in chemical patents using ensemble of contextual language models
Table 3: Official F1-scores of our submissions
['[BOLD] Entity', '[BOLD] CRF  [BOLD] exact', '[BOLD] CRF  [BOLD] relaxed', '[BOLD] bert-base-cased  [BOLD] exact', '[BOLD] bert-base-cased  [BOLD] relaxed', '[BOLD] Ensemble  [BOLD] exact', '[BOLD] Ensemble  [BOLD] relaxed']
We perform the evaluation in the test set released (9,999 files containing chemical narratives from patents) where 3 runs were allowed. For run 1, we submitted our baseline on CRF. For run 2, we used bert-base-cased and for run 3, our ensemble based on the majority vote approach. The ensemble achieved 92.30% of exact F1-score exceeding in 11.74% our baseline and 1.32% the best individual contextualized language model (bert-base-cased).