Machine learning and word sense disambiguation in the biomedical domain: design and evaluation issues.

ABSTRACT:

Background

Word sense disambiguation (WSD) is critical in the biomedical domain for improving the precision of natural language processing (NLP), text mining, and information retrieval systems because ambiguous words negatively impact accurate access to literature containing biomolecular entities, such as genes, proteins, cells, diseases, and other important entities. Automated techniques have been developed that address the WSD problem for a number of text processing situations, but the problem is still a challenging one. Supervised WSD machine learning (ML) methods have been applied in the biomedical domain and have shown promising results, but the results typically incorporate a number of confounding factors, and it is problematic to truly understand the effectiveness and genera

SUBMITTER: Xu H 

PROVIDER: S-EPMC1550263 | biostudies-literature | 2006 Jul

REPOSITORIES: biostudies-literature