Biomedical relation extraction with pre-trained language representations and minimal task-specific architecture
Abstract
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.
- Publication:
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arXiv e-prints
- Pub Date:
- September 2019
- DOI:
- 10.48550/arXiv.1909.12411
- arXiv:
- arXiv:1909.12411
- Bibcode:
- 2019arXiv190912411T
- Keywords:
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- Computer Science - Computation and Language
- E-Print:
- EMNLP-IJCNLP 2019: International Workshop on BioNLP Open Shared Tasks 2019