  We analyze a few of the commonly used statistics based and machine learning
algorithms for natural language disambiguation tasks and observe that they can
be re-cast as learning linear separators in the feature space. Each of the
methods makes a priori assumptions, which it employs, given the data, when
searching for its hypothesis. Nevertheless, as we show, it searches a space
that is as rich as the space of all linear separators. We use this to build an
argument for a data driven approach which merely searches for a good linear
separator in the feature space, without further assumptions on the domain or a
specific problem.
  We present such an approach - a sparse network of linear separators,
utilizing the Winnow learning algorithm - and show how to use it in a variety
of ambiguity resolution problems. The learning approach presented is
attribute-efficient and, therefore, appropriate for domains having very large
number of attributes.
  In particular, we present an extensive experimental comparison of our
approach with other methods on several well studied lexical disambiguation
tasks such as context-sensitive spelling correction, prepositional phrase
attachment and part of speech tagging. In all cases we show that our approach
either outperforms other methods tried for these tasks or performs comparably
to the best.
