Opinion: Strategy of Semi-Automatically Annotating a Full-Text Corpus of Genomics & Informatics
- Author:
Hyun Seok PARK
1
Author Information
- Publication Type:Editorial
- Keywords: biomedical text mining; corpus; text analytics
- MeSH: Genomics; Informatics
- From:Genomics & Informatics 2018;16(4):e40-
- CountryRepublic of Korea
- Language:English
- Abstract: There is a communal need for an annotated corpus consisting of the full texts of biomedical journal articles. In response to community needs, a prototype version of the full-text corpus of Genomics & Informatics, called GNI version 1.0, has recently been published, with 499 annotated full-text articles available as a corpus resource. However, GNI needs to be updated, as the texts were shallow-parsed and annotated with several existing parsers. I list issues associated with upgrading annotations and give an opinion on the methodology for developing the next version of the GNI corpus, based on a semi-automatic strategy for more linguistically rich corpus annotation.
