Biomedical relation extraction with pre-trained language representations and minimal task-specific architecture
Ashok Thillaisundaram, Theodosia Togia · 2019
This paper presents our participation in the AGAC Track from the 2019 BioNLP Open Shared Tasks.We provide a solution for Task 3, which aims to extract "gene -function change -disease" triples, where "gene" and "disease" are mentions of particular genes and diseases respectively and "function change" is one of four pre-defined relationship types.Our system extends BERT (Devlin et al., 2018), a state-of-the-art language model, which learns contextual language representations from a large unlabelled corpus and whose parameters can be fine-tuned to solve specific tasks with minimal additional architecture.We encode the pair of mentions and their textual context as two consecutive sequences in BERT, separated by a special symbol.We then use a single linear layer to classify their relationship into five classes (four pre-defined, as well as 'no relation').Despite considerable class imbalance, our system significantly outperforms a random baseline while relying on an extremely simple setup with no specially engineered features.