Extraction of causal relations between biomedical entities in the form of Biological
Expression Language (BEL) poses a new challenge to the community of biomedical text …

The BioCreative-V community proposed a challenging task of automatic extraction of causal
relation network in Biological Expression Language (BEL) from the biomedical literature …

Causal relation extraction of biomedical entities is one of the most complex tasks in
biomedical text mining, which involves two kinds of information: entity relations and entity …

The identification of causal relationships between events or entities within biomedical texts
is of great importance for creating scientific knowledge bases and is also a fundamental …

Success in extracting biological relationships is mainly dependent on the complexity of the
task as well as the availability of high-quality training data. Here, we describe the new …

Biological expression language (BEL) is one of the most popular languages to represent the
causal and correlative relationships among biological events. Automatically extracting and …

Abstract Biomedical Event Causal Relation Extraction (BECRE) is a challenging task in
biological information extraction and plays a crucial role to serve for knowledge base and …

Automatic extraction of biological network information is one of the most desired and most
complex tasks in biological text mining. The BioCreative track 4 provides training data and …

Causal relations are one of the most important types of information that can be extracted
from medical publications. Therefore, the automated extraction of such relations from …

Extracting meaningful relationships with semantic significance from biomedical literature is
often a challenging task. BioCreative V track4 challenge for the first time has organized a …