ABSTRACT Motivation: Word Sense Disambiguation (WSD), automatically identifying the meaning of ambiguous words in context, is an important stage of text processing. This paper presents a graph-based approach to WSD in the biomedical domain. The method is unsupervised and does not require any labeled training data. It makes use of knowledge from the Unified Medical Language System (UMLS) Metathesaurus which is represented as a graph. A state-of-the-art algorithm, Personalized PageRank, is used to perform WSD. Results: When evaluated on the NLM-WSD dataset the algorithm outperforms other methods that rely on the UMLS Metathesaurus alone. Availability: The WSD system is open source licensed and available from http://ixa2.si.ehu.es/ukb/. The UMLS, MetaMap program and NLM-WSD corpus are available from the National Library of Medicine http://www.nlm.nih.gov/research/umls/, http://mmtx.nlm.nih.gov and http://wsd.nlm.nih.gov. Software to convert the NLM-WSD corpus into a format that can be used by our WSD system is available from http://www.dcs.shef.ac.uk/∼marks/biomedical wsd/ under open source license.
Agirre et al. proposed a graph-based WSD technique which is considered unsupervised but relies on UMLS REF .
Graph-based Word Sense Disambiguation of biomedical documents
Bioinformatics
Bioinformatics
Computer Science Medicine