Simple tricks for improving pattern-based information extraction from the biomedical literature.

ABSTRACT:

Background

Pattern-based approaches to relation extraction have shown very good results in many areas of biomedical text mining. However, defining the right set of patterns is difficult; approaches are either manual, incurring high cost, or automatic, often resulting in large sets of noisy patterns.

Results

We propose several techniques for filtering sets of automatically generated patterns and analyze their effectiveness for different extraction tasks, as defined in the recent BioNLP 2009 shared task. We focus on simple methods that only take into account the complexity of the pattern and the complexity of the texts the patterns are applied to. We show that our techniques, despite their simplicity, yield large improvements in all tasks we analyzed. For instance, they raise

SUBMITTER: Nguyen QL 

PROVIDER: S-EPMC2955645 | biostudies-literature | 2010 Sep

REPOSITORIES: biostudies-literature