  Information extraction techniques, including named entity recognition (NER)
and relation extraction (RE), are crucial in many domains to support making
sense of vast amounts of unstructured text data by identifying and connecting
relevant information. Such techniques can assist researchers in extracting
valuable insights. In this paper, we introduce the Entity-aware Masking for
Biomedical Relation Extraction (EMBRE) method for biomedical relation
extraction, as applied in the context of the BioRED challenge Task 1, in which
human-annotated entities are provided as input. Specifically, we integrate
entity knowledge into a deep neural network by pretraining the backbone model
with an entity masking objective. We randomly mask named entities for each
instance and let the model identify the masked entity along with its type. In
this way, the model is capable of learning more specific knowledge and more
robust representations. Then, we utilize the pre-trained model as our backbone
to encode language representations and feed these representations into two
multilayer perceptron (MLPs) to predict the logits for relation and novelty,
respectively. The experimental results demonstrate that our proposed method can
improve the performances of entity pair, relation and novelty extraction over
our baseline.
