Table 3.

Overall medication component information predictions metrics by models.

Modela	F-measure	Precision	Recall	Slot error rate	Insertion error rate	Deletion error rate	Type error rate	Frontier error rate
RBSb	79.41	94.67	72.28	0.29	0.03	0.23	0.02	0.04
BiLSTMc	73.93	83.89	67.57	0.45	0.09	0.25	0.07	0.15
BiLSTM + FTd	88.08	89.48	87.17	0.21	0.07	0.08	0.03	0.09
BiLSTM + ELMoe	88.03	88.81	87.38	0.24	0.1	0.08	0.03	0.1
BiLSTM + RBS	83.74	88.46	80.24	0.27	0.08	0.13	0.03	0.09
BiLSTM + FT + RBS	88.18	91.73	85.54	0.21	0.07	0.09	0.01	0.07
BiLSTM + ELMo + RBS	89.86	90.83	89.17	0.19	0.09	0.05	0.03	0.08
BiLSTM-CRFf	70.12	79.04	65.57	0.53	0.11	0.26	0.11	0.21
BiLSTM-CRF + FT	87.16	88.58	86.41	0.25	0.09	0.08	0.03	0.12
BiLSTM-CRF + ELMo	88.66	87.95	89.44	0.23	0.11	0.06	0.02	0.11
BiLSTM-CRF + RBS	84.16	88.56	80.73	0.27	0.09	0.13	0.03	0.09
BiLSTM-CRF + FT + RBS	87.74	89.72	86.25	0.22	0.08	0.08	0.02	0.09
BiLSTM-CRF + ELMo + RBS	89.3	90.4	88.31	0.20	0.08	0.06	0.02	0.09

^aModels are described according to their components; if neither ELMo nor FT is mentioned, then we used skip-gram embedding.

^bRBS: rule-based system (ie, the outputs are added as extra features to the input of the deep learning module).

^cBiLSTM: bidirectional long short term memory.

^dFT: FastText embedding.

^eELMo: embedding for language model.

^fCRF: conditional random field.