Abstract:SUMMARYThis paper presents a new approach to word spacing problems by mining reliable words from the Web and use them as additional resources. Conventional approaches to automatic word spacing use noisefree data to train parameters for word spacing models. However, the insufficiency and irrelevancy of training examples is always the main bottleneck associated with automatic word spacing. To mitigate the data-sparseness problem, this paper proposes an algorithm to discover reliable words on the Web to expand th… Show more
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