Name
Title | ||
|---|---|---|
A novel joint biomedical event extraction framework via two-level modeling of documents |
Abstract | ||
|---|---|---|
With the rapid development of information technology, the amount of textual data generated in biomedical field becomes larger and larger. Biomedical event extraction, which is a fundamental information extraction task, has gained a growing interest in biomedical community. Although researchers have proposed various approaches to this task, the performance is still undesirable since previous approaches fail to model biomedical documents effectively. In this paper, we propose an end-to-end framework for document-level joint biomedical event extraction. To better capture the complex relationships among contexts in biomedical documents, a two-level modeling approach is introduced for biomedical documents. More specifically, the dependency-based GCN and hypergraph are used to model local context and global context in each biomedical document, respectively. In addition, a fine-grained interaction mechanism is proposed to model effectively the interaction between local and global contexts to learn better contextualized representations for biomedical event extraction. Comprehensive experiments on two widely used datasets are conducted and the results demonstrate the effectiveness of the proposed framework over state-of-the-art methods. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.ins.2020.10.047 | Information Sciences |
Keywords | DocType | Volume |
Joint biomedical event extraction,Graph convolutional network,Hypergraph,Document-level | Journal | 550 |
ISSN | Citations | PageRank |
0020-0255 | 1 | 0.35 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Weizhong Zhao | 1 | 1 | 3.73 |
| Jinyong Zhang | 2 | 1 | 2.04 |
| Jincai Yang | 3 | 14 | 4.72 |
| Tingting He | 4 | 14 | 9.19 |
| Huifang Ma | 5 | 290 | 29.69 |
| Zhixin Li | 6 | 111 | 24.43 |