Knowledge of the molecular interactions of biological and chemical entities and 
their involvement in biological processes or clinical phenotypes is important 
for data interpretation. Unfortunately, this knowledge is mostly embedded in the 
literature in such a way that it is unavailable for automated data analysis 
procedures. Biological expression language (BEL) is a syntax representation 
allowing for the structured representation of a broad range of biological 
relationships. It is used in various situations to extract such knowledge and 
transform it into BEL networks. To support the tedious and time-intensive 
extraction work of curators with automated methods, we developed the BEL track 
within the framework of BioCreative Challenges. Within the BEL track, we provide 
training data and an evaluation environment to encourage the text mining 
community to tackle the automatic extraction of complex BEL relationships. In 
2017 BioCreative VI, the 2015 BEL track was repeated with new test data. 
Although only minor improvements in text snippet retrieval for given statements 
were achieved during this second BEL task iteration, a significant increase of 
BEL statement extraction performance from provided sentences could be seen. The 
best performing system reached a 32% F-score for the extraction of complete BEL 
statements and with the given named entities this increased to 49%. This time, 
besides rule-based systems, new methods involving hierarchical sequence labeling 
and neural networks were applied for BEL statement extraction.