Extracting Data Records from Unstructured Biomedical Full Text

In this paper, we address the problem of extracting data records and their attributes from unstructured biomedical full text. There has been little effort reported on this in the research community. We argue that semantics is important for record extraction or finer-grained language processing tasks. We derive a data record template including semantic language models from unstructured text and represent them with a discourse level Conditional Random Fields (CRF) model. We evaluate the approach from the perspective of Information Extraction and achieve significant improvements on system performance compared with other baseline systems.

• Type: Conference
• Year: 2007
• Where: EMNLP
• Authors: Donghui Feng, Gully Burns, Eduard H. Hovy
• Keywords: Data Record, Data Record Template, EMNLP 2007, Finer-grained Language Processing, Natural Language Processing