A Relation Extraction Framework for Biomedical Text Using Hybrid Feature Set

Table 6

Results of all feature sets using classification algorithms.

Relation	Feature set	Classification performance of all feature sets
		Setting 1				Setting 2				Setting 3
		Algo	FS	P	R	Algo	FS	P	R	Algo	FS	P	R
Cure	BOW	SVM-RBF	84.76	85.07	84.46	SVM-RBF	97.87	96.38	99.4	SVM-RBF	97.58	95.82	99.4
		SVM	85.1	86.64	83.61	SVM	97.99	96.28	99.76	SVM	97.45	96.13	98.8
		NB	82.88	84.48	81.33	NB	97.26	96.34	98.19	NB	96.45	94.77	98.19
	BOW + NLP	SVM-RBF	84.61	86.14	83.13	SVM-RBF	97.92	96.38	99.52	SVM-RBF	97.29	95.37	99.28
		SVM	84.53	85.84	83.25	SVM	97.74	96.59	98.92	SVM	97.03	95.67	98.43
		NB	83.64	83.8	83.49	NB	97.19	96.44	97.95	NB	96.51	94.88	98.19
	BOW + NLP + UMLS (NP)	SVM-RBF	84.9	85.47	84.34	SVM-RBF	97.99	96.39	99.64	SVM-RBF	97.58	95.82	99.4
		SVM	84.44	86.58	82.41	SVM	97.98	96.6	99.4	SVM	97.08	95.89	98.31
		NB	82.71	84.81	80.72	NB	97.06	96.42	97.71	NB	96.45	94.66	98.31
	BOW + NLP + UMLS (NP + VP)	SVM-RBF	84.83	85.82	83.86	SVM-RBF	98.05	96.39	99.76	SVM-RBF	97.41	95.49	99.4
		SVM	84.83	86.87	82.89	SVM	97.92	96.6	99.28	SVM	97.08	95.89	98.31
		NB	82.71	84.53	80.96	NB	96.94	96.42	97.47	NB	96.58	94.68	98.55
Prevent relation	BOW	SVM-RBF	78.95	88.24	71.43	SVM-RBF	90.91	94.83	87.3	SVM-RBF	89.06	87.69	90.48
		SVM	81.02	93.75	71.34	SVM	91.81	94.92	88.89	SVM	86.82	84.85	88.89
		NB	60.2	77.5	49.21	NB	91.94	93.44	90.48	NB	91.06	93.33	88.89
	BOW + NLP	SVM-RBF	71.15	90.24	58.73	SVM-RBF	90.75	96.43	85.71	SVM-RBF	86.61	85.94	87.3
		SVM	80	93.62	69.84	SVM	90.91	94.83	87.3	SVM	86.15	83.58	88.89
		NB	63.07	72.92	55.56	NB	93.55	95.08	92.06	NB	88.71	90.16	87.3
	BOW + NLP + UMLS (NP)	SVM-RBF	72.38	90.48	60.32	SVM-RBF	90.75	96.43	85.71	SVM-RBF	88.19	87.5	88.89
		SVM	81.42	92	73.02	SVM	90.91	94.83	87.3	SVM	86.15	83.58	88.89
		NB	62.13	80	50.79	NB	93.55	95.08	92.06	NB	88.72	90.19	87.3
	BOW + NLP + UMLS (NP + VP)	SVM-RBF	72.38	90.48	60.32	SVM-RBF	90.75	96.43	85.71	SVM-RBF	85.94	84.62	87.3
		SVM	80.36	91.84	71.43	SVM	90.91	94.83	87.3	SVM	85.5	82.35	88.89
		NB	62.26	76.74	52.38	NB	93.55	95.08	92.06	NB	88.89	88.89	88.89
Side effect	BOW	SVM-RBF	21.05	50	13.33	SVM-RBF	70	70	70	SVM-RBF	75.86	78.57	73.33
		SVM	21.74	31.25	16.67	SVM	63.16	66.67	60	SVM	70.18	74.07	66.67
		NB	22.22	33.33	16.67	NB	74.2	71.88	76.67	NB	83.54	78.79	88.89
	BOW + NLP	SVM-RBF	25.65	55.56	16.67	SVM-RBF	68.97	71.43	66.67	SVM-RBF	71.18	72.41	70
		SVM	22.73	35.71	16.67	SVM	65.52	67.86	63.33	SVM	67.86	73.08	63.33
		NB	30.43	43.75	23.33	NB	67.69	62.86	73.33	NB	77.42	75	80
	BOW + NLP + UMLS (NP)	SVM-RBF	16.67	50	10	SVM-RBF	66.67	66.67	66.67	SVM-RBF	74.57	75.86	73.33
		SVM	18.18	28.57	13.33	SVM	62.07	64.29	60	SVM	67.86	73.08	63.33
		NB	13.64	21.43	10	NB	68.75	64.71	73.33	NB	88.71	90.16	87.3
	BOW + NLP + UMLS (NP + VP)	SVM-RBF	21.62	57.14	13.33	SVM-RBF	70.18	74.07	66.67	SVM-RBF	68.97	71.43	66.67
		SVM	22.22	33.33	16.67	SVM	65.52	67.86	63.33	SVM	65.45	72	60
		NB	26.67	40	20	NB	67.69	62.86	73.33	NB	88.89	88.89	88.89

Bag of word (BOW) is unigrams only.
Natural language processing (NLP) is noun and verb phrases.
UMLS (NP) is the use of UMLS with noun phrase ranking only.
UMLS (NP + VP) is the use of UMLS with noun phrase and verb phrase ranking.
SVM is Support Vector Machine algorithms; SVM-RBF is Support Vector Machine-Radial Based Function algorithm; NB is Naïve Bayes algorithm; FS is -score; P is precision; R is recall.