Abstract
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.
Highlights
A biological network such as a protein–protein interaction network or a gene regulatory network is a unique way of representing a biological system
One sentence may contain more than one Biological expression language (BEL) statement
This article describes the construction of BelSmile, a system that can automatically extract BEL statements
Summary
A biological network such as a protein–protein interaction network or a gene regulatory network is a unique way of representing a biological system. Investigation of such networks is an important task in the field of life science. The rapid growth of research publications makes it difficult to keep track of novel networks or update existing ones.
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