  Memory-based learning (MBL) has enjoyed considerable success in corpus-based
natural language processing (NLP) tasks and is thus a reliable method of
getting a high-level of performance when building corpus-based NLP systems.
However there is a bottleneck in MBL whereby any novel testing item has to be
compared against all the training items in memory base. For this reason there
has been some interest in various forms of memory editing whereby some method
of selecting a subset of the memory base is employed to reduce the number of
comparisons. This paper investigates the use of a modified self-organising map
(SOM) to select a subset of the memory items for comparison. This method
involves reducing the number of comparisons to a value proportional to the
square root of the number of training items. The method is tested on the
identification of base noun-phrases in the Wall Street Journal corpus, using
sections 15 to 18 for training and section 20 for testing.
