Named entity recognition in chemical patents using ensemble of contextual language models
Table 5: Evaluation results (F1) on development set – Exact scores
['[BOLD] Entity', '[BOLD] CRF', '[BOLD] CNN', '[BOLD] bert-base -cased', '[BOLD] bert-base -uncased', '[BOLD] bert-large -cased', '[BOLD] bert-large -uncased', '[BOLD] ChemBERTa', '[BOLD] Ensemble']
The gold annotations for the test set are not available, thus we performed our error analysis on the development set. Among all models, ChemBERTa achieves the lowest performance. All the BERT-based models outperform the baseline models for all classes. The ensemble model consistently outperforms the single models. The ensemble model achieves the highest improvement for reaction_product and starting_material with over 12-point increase in F1-score.