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.

• Type: Journal
• Year: 2002
• Where: JMLR
• Authors: Tong Zhang, Fred Damerau, David Johnson
• Keywords: Classification Problems, JMLR 2002, Winnow, Winnow Algorithm