Name
Abstract | ||
|---|---|---|
In biomédical event extraction domain, there is a small amount of labeled data along with a large pool of unlabeled data. Many supervised learning algorithms for bio-event extraction have been affected by the data sparseness. In this paper, we present a new solution to perform biomédical event extraction from scientific documents, applying a semi-supervised approach to extract features from unlabeled data using labeled data features as a reference. This strategy is evaluated via experiments in which the data from the BioNLP2011 and PubMed are applied. To the best of our knowledge, it is the first time that the combination of labeled and unlabeled data are used for biomédical event extraction and our experimental results demonstrate the state-of-the-art performance in this task. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/BIBMW.2012.6470206 | BIBM Workshops |
Keywords | DocType | Citations |
scientific document,bio-event extraction,data sparseness,new solution,dical event extraction,large pool,dical event extraction domain,unlabeled data,experimental result,data feature | Conference | 0 |
PageRank | References | Authors |
0.34 | 12 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hongfei Lin | 1 | 768 | 122.52 |
| Yanpeng Li | 2 | 0 | 0.34 |
| Jian Wang | 3 | 73 | 16.74 |
| Qian Xu | 4 | 0 | 0.34 |
| Zhihao Yang | 5 | 270 | 36.04 |