  The eventual goal of a language model is to accurately predict the value of a
missing word given its context. We present an approach to word prediction that
is based on learning a representation for each word as a function of words and
linguistics predicates in its context. This approach raises a few new questions
that we address. First, in order to learn good word representations it is
necessary to use an expressive representation of the context. We present a way
that uses external knowledge to generate expressive context representations,
along with a learning method capable of handling the large number of features
generated this way that can, potentially, contribute to each prediction.
Second, since the number of words ``competing'' for each prediction is large,
there is a need to ``focus the attention'' on a smaller subset of these. We
exhibit the contribution of a ``focus of attention'' mechanism to the
performance of the word predictor. Finally, we describe a large scale
experimental study in which the approach presented is shown to yield
significant improvements in word prediction tasks.
