Domain transformation on biological event extraction by learning methods

Wen Juan Hou, Bamfa Ceesay

研究成果: 雜誌貢獻期刊論文同行評審

摘要

Event extraction and annotation has become a significant focus of recent efforts in biological text mining and information extraction (IE). However, event extraction, event annotation methods, and resources have so far focused almost exclusively on a single domain. State-of-the-art studies on biological event extraction and annotation are typically domain-dependent and domain-restricted. In this paper, we adopt an approach aimed at extracting events and relations for two different tasks by generating a common dataset using transfer learning and structural correspondence learning (SCL). A deep learning event extraction system was developed to evaluate our results. Our approach comprises two stages: (1) generating a dataset from two independent event extraction tasks or domains, and (2) using a classifier model to learn feature patterns from the generated dataset for event and relation extraction. The classifier in the proposed model can extract events and relations irrespective of the domain of the test input. Our study shows that this approach performs competitively compared to domain specific or dependent tasks.

原文英語
文章編號103236
期刊Journal of Biomedical Informatics
95
DOIs
出版狀態已發佈 - 2019 七月

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Informatics

指紋 深入研究「Domain transformation on biological event extraction by learning methods」主題。共同形成了獨特的指紋。

引用此