Learning A Unified Named Entity Tagger From Multiple Partially Annotated Corpora For Efficient Adaptation
Table 6: Local evaluation (f1 scores). The best results that are significantly better than the second best are bold-faced, while those are best but not significantly better than the second best are underlined. All the significance tests are conducted using mention-level McNemar’s Chi-square test, with p-value = 0.01.
['[BOLD] Corpus', '[BOLD] BC2GM', '[BOLD] BC4CHM', '[BOLD] NCBI', '[BOLD] JNLPBA', '[BOLD] Linnaeus']
For a sanity check, we evaluate the models on the training corpora and compare the results with state-of-the-art systems. In this setting, all the models are trained on the training set of the training corpora (without fine-tuning on global evaluation corpora) and evaluated on their test set. STM is the single-task models we implemented, following the settings in \newcitewang2018cross. The SOTA is achieved by \newcitewang2018cross with multi-task model, which is shown in the table as MTM \newcitewang2018cross. They trained their model on BC2GC, BC4CHM, NCBI, JNLPBA, and BC5CDR. MTM (ours) is the multi-task model we trained on our five training corpora and used as a baseline in the global evaluations. It has the same architecture as \newcitewang2018cross.