We start from a baseline joint model that performs the tasks of named entity recognition (NER) and relation extraction at once. Previously proposed models (summarized in Section 2) exhibit several issues that the neural network-based baseline approach (detailed in Section 3.1) overcomes: (i) our model uses automatically extracted features without the need of external parsers nor manually extracted features (see <cite>Gupta et al. (2016)</cite> ; Miwa and Bansal (2016) ; Li et al. (2017) ), (ii) all entities and the corresponding relations within the sentence are extracted at once, instead of examining one pair of entities at a time (see Adel and Schütze (2017) ), and (iii) we model relation extraction in a multi-label setting, allowing multiple relations per entity (see Katiyar and Cardie (2017) ; Bekoulis et al. (2018a) ).