Table 5.

Performance of CRF-based BNER systems when different types of WR features were used.

System	BioCreAtIvE II GM (%)			JNLPBA (%)
	Precision	Recall	F-measure	Precision	Recall	F-measure
Baseline	87.31	69.20	77.21	71.37	68.68	70.00
Baseline + WR1	86.55	73.18	79.31	70.96	71.44	71.20
Baseline + WR2	87.34	73.91	80.07	71.59	69.55	70.55
Baseline + WR3	86.56	72.22	78.74	71.11	69.88	70.49
Baseline + WR1 + WR2	86.56	75.39	80.59	70.99	71.77	71.38
Baseline + WR1 + WR3	85.77	74.65	79.82	70.77	71.87	71.31
Baseline + WR2 + WR3	87.03	74.90	80.51	71.19	70.41	70.80
Baseline + WR1 + WR2 + WR3	86.54	76.05	80.96	70.78	72.00	71.39

*WR1, WR2, and WR3 denote three different types of word representation features: clustering-based, distributional, and word embeddings features, respectively.