Conference Paper Published

Multi-layer Sequence Labeling-Based Joint Biomedical Event Extraction

Chen, G., Wu, P., Gu, J., Qian, L.*, & Zhou, G. (2024). Multi-layer Sequence Labeling-Based Joint Biomedical Event Extraction. In D. F. Wong, Z. Wei, & M. Yang (Eds.), Natural Language Processing and Chinese Computing: 13th National CCF Conference, NLPCC 2024, Hangzhou, China, November 1–3, 2024, Proceedings, Part II, 135-148. Springer.

DOI: https://doi.org/10.1007/978-981-97-9434-8_11

Abstract

In recent years, biomedical event extraction has been dominated by complicated pipeline and joint methods, which need to be simplified. In addition, existing work has not effectively utilized trigger word information explicitly. Hence, we propose MLSL, a method based on multi-layer sequence labeling for joint biomedical event extraction. MLSL does not introduce prior knowledge and complex structures. Moreover, it explicitly incorporates the information of candidate trigger words into the sequence labeling to learn the interaction relationships between trigger words and argument roles. Based on this, MLSL can learn well with just a simple workflow. Extensive experimentation demonstrates the superiority of MLSL in terms of extraction performance compared to other state-of-the-art methods.

Keywords

Biomedical event extraction, Natural language processing, Sequence labeling