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.- Anthology ID:
- D19-5713
- Volume:
- Proceedings of the 5th Workshop on BioNLP Open Shared Tasks
- Month:
- November
- Year:
- 2019
- Address:
- Hong Kong, China
- Editors:
- Kim Jin-Dong, Nédellec Claire, Bossy Robert, Deléger Louise
- Venue:
- BioNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 84–89
- Language:
- URL:
- https://aclanthology.org/D19-5713
- DOI:
- 10.18653/v1/D19-5713
- Cite (ACL):
- Ashok Thillaisundaram and Theodosia Togia. 2019. Biomedical relation extraction with pre-trained language representations and minimal task-specific architecture. In Proceedings of the 5th Workshop on BioNLP Open Shared Tasks, pages 84–89, Hong Kong, China. Association for Computational Linguistics.
- Cite (Informal):
- Biomedical relation extraction with pre-trained language representations and minimal task-specific architecture (Thillaisundaram & Togia, BioNLP 2019)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/D19-5713.pdf