  The classical, vector space model for text retrieval is shown to give better
results (up to 29% better in our experiments) if WordNet synsets are chosen as
the indexing space, instead of word forms. This result is obtained for a
manually disambiguated test collection (of queries and documents) derived from
the Semcor semantic concordance. The sensitivity of retrieval performance to
(automatic) disambiguation errors when indexing documents is also measured.
Finally, it is observed that if queries are not disambiguated, indexing by
synsets performs (at best) only as good as standard word indexing.
