Combining Spans into Entities:A Neural Two-Stage Approach for Recognizing Discontiguous Entities
Table 3: Results on handling multiple entity types.
['[EMPTY]', 'Model', '[ITALIC] P', '[ITALIC] R', '[ITALIC] F1']
Our neural model again achieves the best performance among all models in terms of F1. Compared with neural CRF, our model is significantly better at recalling entities. Similar to the previous observation, the neural encoder consistently boosts the performance of the CRF by recalling more entities, compared with its non-neural counterpart.