Biological expression language (BEL) is one of the most popular languages to 
represent the causal and correlative relationships among biological events. 
Automatically extracting and representing biomedical events using BEL can help 
biologists quickly survey and understand relevant literature. Recently, many 
researchers have shown interest in biomedical event extraction. However, the 
task is still a challenge for current systems because of the complexity of 
integrating different information extraction tasks such as named entity 
recognition (NER), named entity normalization (NEN) and relation extraction into 
a single system. In this study, we introduce our BelSmile system, which uses a 
semantic-role-labeling (SRL)-based approach to extract the NEs and events for 
BEL statements. BelSmile combines our previous NER, NEN and SRL systems. We 
evaluate BelSmile using the BioCreative V BEL task dataset. Our system achieved 
an F-score of 27.8%, ∼7% higher than the top BioCreative V system. The three 
main contributions of this study are (i) an effective pipeline approach to 
extract BEL statements, and (ii) a syntactic-based labeler to extract 
subject-verb-object tuples. We also implement a web-based version of BelSmile 
(iii) that is publicly available at iisrserv.csie.ncu.edu.tw/belsmile.