  Due to the exponential growth of biomedical literature, event and relation
extraction are important tasks in biomedical text mining. Most work only focus
on relation extraction, and detect a single entity pair mention on a short span
of text, which is not ideal due to long sentences that appear in biomedical
contexts. We propose an approach to both relation and event extraction, for
simultaneously predicting relationships between all mention pairs in a text. We
also perform an empirical study to discuss different network setups for this
purpose. The best performing model includes a set of multi-head attentions and
convolutions, an adaptation of the transformer architecture, which offers
self-attention the ability to strengthen dependencies among related elements,
and models the interaction between features extracted by multiple attention
heads. Experiment results demonstrate that our approach outperforms the state
of the art on a set of benchmark biomedical corpora including BioNLP 2009,
2011, 2013 and BioCreative 2017 shared tasks.
