Abstract
Temporal relation annotation in the clinical domain is crucial yet challenging due to its workload and the medical expertise required. In this paper, we propose a novel annotation method that integrates event start-points ordering and question-answering (QA) as the annotation format. By focusing only on two points on a timeline, start-points ordering reduces ambiguity and simplifies the relation set to be considered during annotation. QA as annotation recasts temporal relation annotation into a reading comprehension task, allowing annotators to use natural language instead of the formalisms commonly adopted in temporal relation annotation. Based on our method, most of the relations in a document are inferable from a significantly smaller number of explicitly annotated relations, showing the efficiency of our proposed method. Using these inferred relations, we develop a temporal relation classification model that achieves a 0.72 F1 score. Also, by decomposing the annotation process into QA generation and QA validation, our method enables collaboration among medical experts and non-experts. We obtained high inter-annotator agreement (IAA) scores, which indicate the positive prospect of such collaboration in the annotation process. Our annotated corpus, annotation tool, and trained model are publicly available: https://github.com/seiji-shimizu/qa-start-ordering.- Anthology ID:
- 2024.lrec-main.1171
- Volume:
- Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
- Month:
- May
- Year:
- 2024
- Address:
- Torino, Italia
- Editors:
- Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
- Venues:
- LREC | COLING
- SIG:
- Publisher:
- ELRA and ICCL
- Note:
- Pages:
- 13371–13381
- Language:
- URL:
- https://aclanthology.org/2024.lrec-main.1171
- DOI:
- Cite (ACL):
- Seiji Shimizu, Lis Pereira, Shuntaro Yada, and Eiji Aramaki. 2024. QA-based Event Start-Points Ordering for Clinical Temporal Relation Annotation. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 13371–13381, Torino, Italia. ELRA and ICCL.
- Cite (Informal):
- QA-based Event Start-Points Ordering for Clinical Temporal Relation Annotation (Shimizu et al., LREC-COLING 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.lrec-main.1171.pdf