Table 5.

Protein tagging: impact of distributional semantic features on BANNER.

Rank	Setting	Precision	Recall	F-score	Significance
1	Rank 1 system	88.48	85.97	87.21	6–11
2	Rank 2 system	89.30	84.49	86.83	8–11
3	BANNER_Dict+DistSem	88.25	85.12	86.66	8–11
4	Rank 3 system	84.93	88.28	86.57	8–11
5	BANNER_noDict+DistSem	87.95	85.06	86.48	10–11
6	Rank 4 system	87.27	85.41	86.33	10–11
7	Rank 5 system	85.77	86.80	86.28	10–11
8	Rank 6 system	82.71	89.32	85.89	10–11
9	BANNER_Dict	86.41	84.55	85.47	–
10	Rank 7 system	86.97	82.55	84.70	–
11	BANNER_noDict	85.63	83.10	84.35	–

Notes: The significance column indicates which systems are significantly less accurate than the system in the corresponding row. These values are based on the Bootstrap re-sampling calculations performed as part of the evaluation in the BioCreative II shared task (the latest gene or protein tagging task). BANNER_Dict+DistSem is the system that uses both manual and empirical lexical resources. BANNER_noDict+DistSem is the system that uses only empirical lexical resources. BANNER_Dict is the system that uses only manual lexical resources. This is the system available prior to this research, and the baseline for this study. BANNER_noDict is the system that uses neither manual nor empirical lexical resources. BANNER_Dict+DistSem is the system that is significantly more accurate than the baseline. It is equally important to the improvement that the accuracy of BANNER_noDict+DistSem is better than BANNER_noDict. The most significant contribution in terms of research is that an equivalent accuracy (BANNER_noDict+DistSem and BANNER_Dict) could be achieved even without using any manually compiled lexical resources apart from the annotated corpora.