How Large a Corpus Do We Need: Statistical Method Versus Rule-based Method

Abstract

We investigate the impact of input data scale in corpus-based learning using a study style of Zipf’s law. In our research, Chinese word segmentation is chosen as the study case and a series of experiments are specially conducted for it, in which two types of segmentation techniques, statistical learning and rule-based methods, are examined. The empirical results show that a linear performance improvement in statistical learning requires an exponential increasing of training corpus size at least. As for the rule-based method, an approximate negative inverse relationship between the performance and the size of the input lexicon can be observed.

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