  In this paper, we use a feature model of the semantics of plural determiners
to present an approach to grammar checking for definiteness. Using neural
network techniques, a semantics -- morphological category mapping was learned.
We then applied a textual encoding technique to the 125 occurences of the
relevant category in a 10 000 word narrative text and learned a surface --
semantics mapping. By applying the learned generation function to the newly
generated representations, we achieved a correct category assignment in many
cases (87 %). These results are considerably better than a direct surface
categorization approach (54 %), with a baseline (always guessing the dominant
category) of 60 %. It is discussed, how these results could be used in
multilingual NLP applications.
