Combining Spans into Entities:A Neural Two-Stage Approach for Recognizing Discontiguous Entities
Table 2: Main results. Graph: the hypergraph based model by muis2016learning. “enough” and “all” denotes the heuristics used in ambiguous model. w.o. shared encoder: without using shared encoder.
['[EMPTY]', 'Model', '[ITALIC] P', '[ITALIC] R', '[ITALIC] F1']
In both non-neural and neural settings, our model achieves the better result in terms of F1 compared with other baselines, revealing the effectiveness of our methodology of decomposing the task into two stages. Our neural model achieves the best performance even without using any external handcrafted features.