Paper: Scaling up WSD with Automatically Generated Examples

ACL ID W12-2429
Title Scaling up WSD with Automatically Generated Examples
Venue Workshop on Biomedical Natural Language Processing
Session
Year 2012
Authors

The most accurate approaches to Word Sense Disambiguation (WSD) for biomedical docu- ments are based on supervised learning. How- ever, these require manually labeled training examples which are expensive to create and consequently supervised WSD systems are normally limited to disambiguating a small set of ambiguous terms. An alternative approach is to create labeled training examples automat- ically and use them as a substitute for manu- ally labeled ones. This paper describes a large scale WSD system based on automatically la- beled examples generated using information from the UMLS Metathesaurus. The labeled examples are generated without any use of la- beled training data whatsoever and is therefore completely unsupervised (unlike some previ- ous approaches). The system is evaluated o...