Under CopyrightAli, MehdiMehdiAliMadan, SumitSumitMadanFischer, AsjaAsjaFischerPetzka, HenningHenningPetzkaFluck, JulianeJulianeFluck2022-03-1326.6.20182017https://publica.fraunhofer.de/handle/publica/40072810.24406/publica-fhg-400728The automatic extraction of biomedical relations and entities from text has become extremely important in systems biology. For coding the extracted information, the Biological Expression Language (BEL) can be used. A BEL-statement consists of a subject (entity), a predicate (type of relationship), and an object (entity or a further BEL-statement). This paper describes a system based on neural networks (NNs) to extract BEL-statements in the context of the BioCreAtivE 2017 track 3 (task 1) challenge. In our approach, the overall problem is divided into four subtasks: (i) the detection of named entities (NER), (ii) deciding whether a pair of entities participate in a relation, (iii) determining which of the entities participating in a relation is the subject/object entity, and (iv) extracting the type of the relation. By merging the solutions of the subtasks, the BEL-statements are generated. Except for the named entity recognition, (convolutional) NNs were used to solve the tasks. The results show that a neural net based approach is reasonable to use for the extraction of biomedical relations. The limitations of our system are related to the small size (compared to other NN-based applications) of the data set. We argue that by overcoming this limitation, promising results can be expected from NN-based approaches in future.en003005006518conference paper