Extraction of data deposition statements from the literature: a method for automatically tracking research results.

ABSTRACT:

Motivation

Research in the biomedical domain can have a major impact through open sharing of the data produced. For this reason, it is important to be able to identify instances of data production and deposition for potential re-use. Herein, we report on the automatic identification of data deposition statements in research articles.

Results

We apply machine learning algorithms to sentences extracted from full-text articles in PubMed Central in order to automatically determine whether a given article contains a data deposition statement, and retrieve the specific statements. With an Support Vector Machine classifier using conditional random field determined deposition features, articles containing deposition statements are correctly identified with 81% F-measure. An error an

SUBMITTER: Neveol A 

PROVIDER: S-EPMC3223368 | biostudies-literature | 2011 Dec

REPOSITORIES: biostudies-literature