Text Chunking based on a Generalization of Winnow

  This paper describes a text chunking system based on 
  a generalization of the Winnow algorithm. We propose
  a general statistical model for text chunking which we then convert
  into a classification problem. We argue that the Winnow family of
  algorithms is particularly suitable for solving classification
  problems arising from NLP applications, due to their robustness to
  irrelevant features.
  However in theory, Winnow may not converge for linearly non-separable data. 
  To remedy this problem, we employ a generalization of the original Winnow
  method. 
  An additional advantage of the new algorithm is that it provides reliable 
  confidence estimates for its classification predictions. This property
  is required in our statistical modeling approach.
  We show that our system achieves state of the art performance in 
  text chunking with less computational cost then previous systems.

