Eckhard Bick - VISL

Therefore, there is a case for introducing "real" (i.e. not primarily syntactic) semantic
classes on top of “mappable” (i.e. syntax or morphology based), semantic distinctions.
But which and how many semantic tag categories should be chosen? And how
should they be organised with regard to each other? When answering this question, it is
important to make the teleological distinction between defining meaning and
distinguishing meaning. In a system that does not claim to understand text, but only to
structure or translate it, the final semantics lies (only) in the eye of the beholder.
Therefore, in order to make a sense distinction, it isn't necessary to define a given
sense, or identify its referent, - it is enough to draw a line across the semantic map
which separates two different senses of a word. If a parser can determine - in a given
text - just which side of the line a given usage has to be placed, then a disambiguated
tag will allow the system to pick the exact word sense from the lexicon – the sense need
not be in the tag itself. Constraint Grammar is an ideal tool to make such choices in a
context dependent and robust way. Like on the morphological and syntactic level, CG
rules will not define but disambiguate from a given set of distinctions. Semantic tags,
then, have to reflect the structure of the semantic landscape rather than the meaning of
individual words. Consequently, for a CG to work well, the system’s set of semantic
tags should be inspired by some ordering or classification principle for word senses.
Word meanings – to mention some basic systems - can be ordered in autonomous
groups (word fields, picture dictionaries), semantic hierarchies (monolingual thesauri,
biological classification, Princetown WordNet) or word nets (multilingual relational
systems like EuroWordNet). Finally, word meanings can be ordered by semantic
decomposition:
vivo
-> morto = ‘NOT alive’
viver = ‘BE alive’
-> nascer = BECOME alive’
-> morrer = ‘BECOME NOT alive’
-> matar = ‘MAKE BECOME NOT alive
I believe that the most cost efficient 217 way to draw distinction lines across the semantic
landscape is by prototype similarity. Prototypes can be conceived as both class
hyperonyms (‘animal’: dog, pig, lion, ‘plant’: oak, sunflower) and common well known
set members of classes otherwise too “abstract” (‘knife’ for ‘cutting tools’: knife, sword,
saber, ’book’ for ‘readables’: book, paper, magazine). Whatever the abstraction level,
prototype similarity testing is done by asking ‘is it more like A or more like B?’ rather
217 On the one hand, in terms of CG rule efficiency, on the other, in terms of the work load needed to enter the relevant
information into the lexion.
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