This paper presents a new general supervised word sense disambiguation method based on a relatively small syntactically parsed and semantically tagged training corpus.The method exploits a full sentential context and all the explicit semantic relations in a sentence to identify the senses of all of that sentence's content words. It solves the sparse data problem of a small training corpus by substituting the words by their semantic classes.In spite of a very small training corpus, we report an overall accuracy of 80.3% (85.7, 63.9, 83.6 and 86.5%, for nouns, verbs, adjectives and adverbs, respectively), which exceeds the accuracy of a statistical sense-frequency based semantic tagging, the only really applicable general disambiguating technique. Because the method uses the sentential syntactic structure it is particularly suitable for integration with a probabilistic syntactic analyser.