  This squib claims that Large-scale Automatic Sense Tagging of text (LAST) can
be done at a high-level of accuracy and with far less complexity and
computational effort than has been believed until now. Moreover, it can be done
for all open class words, and not just carefully selected opposed pairs as in
some recent work. We describe two experiments: one exploring the amount of
information relevant to sense disambiguation which is contained in the
part-of-speech field of entries in Longman Dictionary of Contemporary English
(LDOCE). Another, more practical, experiment attempts sense disambiguation of
all open class words in a text assigning LDOCE homographs as sense tags using
only part-of-speech information. We report that 92% of open class words can be
successfully tagged in this way. We plan to extend this work and to implement
an improved large-scale tagger, a description of which is included here.
