Incorporating rich background knowledge for gene named entity classification and recognition

BMC Bioinformatics

Table 8 Comparison of performance and applicability of different NER systems on BioCreative 2 test set

System or authors	Precision	Recall	F-score	# of features	Tagging complexity	Availability
CRF 1 (ABNER+)	87.30	80.68	83.86	171,251	LM	N
CRF 2 (ABNER++)	87.39	81.96	84.59	355,461	LM	N
Dictionary	90.37	82.40	86.20	0	Trie	Y
Dictionary + CRF 2	90.52	87.63	89.05	355,609	LM	Y
BANNER [26, 27]	88.66	84.32	86.43	500,876	LM+POS tagger	Y
Ando [5] (1^st in BioCreative 2)	88.48	85.97	87.21	--	2*LM+POS tagger+syntactic parser	N
Hus et al. [10]	88.95	87.65	88.30	8 * 5,059,368	8*LM+POS tagger	N

In the 6^th column, 'LM' and 'Trie' respectively refer to the time complexities of a linear model and a Trie tree based dictionary match. The 'Dictionary' method doesn't need any feature, once the dictionary is constructed. For Ando's system, we cannot find the number of features in the paper [5]. Since the systems in the last two rows used classifier combination, the tagging complexities and numbers of features are multiplied by the numbers of sub-models.

ISSN: 1471-2105