Learning A Unified Named Entity Tagger From Multiple Partially Annotated Corpora For Efficient Adaptation
Table 2: Results for task adaptation in the no-supervision setting. The best f1 score in each column that is significantly better than the second best is bold-faced, while those are better but not significantly are underlined. All the significance tests are conducted using mention-level McNemar’s Chi-square test, with p-value = 0.01.
['[BOLD] Corpus', '[BOLD] Trained on Other Biomedical Datasets  [BOLD] BC5CDR', '[BOLD] Trained on Other Biomedical Datasets  [BOLD] BC5CDR', '[BOLD] Trained on Other Biomedical Datasets  [BOLD] BC5CDR', '[BOLD] Trained on Other Biomedical Datasets  [BOLD] BioNLP13CG', '[BOLD] Trained on Other Biomedical Datasets  [BOLD] BioNLP13CG', '[BOLD] Trained on Other Biomedical Datasets  [BOLD] BioNLP13CG', '[BOLD] Trained on Other Biomedical Datasets  [BOLD] BioNLP11ID', '[BOLD] Trained on Other Biomedical Datasets  [BOLD] BioNLP11ID', '[BOLD] Trained on Other Biomedical Datasets  [BOLD] BioNLP11ID', '[BOLD] Traind on CoNLL  [BOLD] CoNLL 2003', '[BOLD] Traind on CoNLL  [BOLD] CoNLL 2003', '[BOLD] Traind on CoNLL  [BOLD] CoNLL 2003']
We report precision and recall in addition to f1 scores to better show the differences between the models.