Linear Interpolated Methods in Statistical Natural Language Processing
统计自然语言处理中的线性插值平滑技术

One of the complicated problems in statistical natural language processing is the data-sparseness problem. There are mainly two kinds of smoothing technologies to solve it, backing-off models and linear interpolated models. This article compares several typical linear interpolated methods, and focuses on studying the relationship between the smoothing parameters and the parts of speech. Besides, two improved methods are proposed. Our experiment results show that both of them outperform original ones.