Joint entity and relation extraction: Joint models (Li and Ji, 2014; Miwa and Sasaki, 2014) that are based on manually extracted features have been proposed for performing both the named entity recognition (NER) and relation extraction subtasks at once. These methods rely on the availability of NLP tools (e.g., POS taggers) or manually designed features leading to additional complexity. Neural network methods have been exploited to overcome this feature design issue and usually involve RNNs and CNNs<cite> (Miwa and Bansal, 2016</cite>; Zheng et al., 2017) . Specifically, <cite>Miwa and Bansal (2016)</cite> as well as Li et al. (2017) apply bidirectional tree-structured RNNs for different contexts (i.e., news, biomedical) to capture syntactic information (using external dependency parsers).