Abstract

The SNoW (Sparse Network of Winnows) architecture has recently been successful applied to a number of natural language processing (NLP) problems. In this paper, we propose large margin versions of the Winnow algorithms, which we argue can potentially enhance the performance of basic Winnows (and hence the SNoW architecture). We demonstrate that the resulting methods achieve performance comparable with support vector machines for text categorization applications. We also explain why both large margin Winnows and SVM can be suitable for NLP tasks. 1. INTRODUCTION Recently there have been considerable interests in applying machine learning techniques to problems in natural language processing. One method that has great success in many applications is the SNoW architecture [5, 12]. This architecture is based on the Winnow algorithm [15], which in theory is suitable for problems with many irrelevant attributes. The success of SNoW is then attributed to the argument that typical NLP tasks ...

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