To solve the problem of sparse data, we need more data... obviously.
为了解决数据稀缺问题，我们需要更多数据。
The present smoothing techniques deal with the data sparse problem using different discount and compensate strategy, and they have different merit or shortcoming on complexity and rationality.
现有平滑技术利用不同的折扣和补偿策略来处理数据稀疏问题，在计算复杂性与合理性方面各有其优缺点。
The present smoothing techniques have solved the data sparse problem effectively but have not further analyzed the reasonableness for the frequency distribution of events occurring.
现有平滑技术虽然已有效地对数据稀疏问题进行了处理，但对已出现事件频率分布的合理性并没有作出有效的分析。
We introduce and motivate the main theme of the course, the setting of the problem of learning from examples as the problem of approximating a multivariate function from sparse data - the examples.
我们介绍且激发课程的主题将朝向于实例学习法的问题设定，例如稀疏值中多变量函数近似的问题。
Based on trigram models, this paper proposes a three-step method of "word-similar word-part of speech" by incorporating the similar words and solves the problem of sparse data to a large extent.
本文利用三元模型，通过引入相似词，采取“词形-相似词-词性”三步回退的策略，比较好地缓解了数据稀疏问题。
These two methods are both not subject to external resource constraints and would solve the data sparse problem in some extent.
这两种方法均不受外部资源所限，能在一定程度上解决数据稀疏问题。
We introduce and motivate the main theme of the course, setting the problem of learning from examples as the problem of approximating a multivariate function from sparse data.
我们引出本课程的主题，把从样例中学习的问题转化成从稀疏资料中逼近多变数函数的问题。
The key problem in N-gram method is the problem of sparse data which still can not be solved effectively now.
但是随着研究深入，出现稀疏数据成像问题，无法用传统方法重建清晰图像。
The key problem in N-gram method is the problem of sparse data which still can not be solved effectively now.
但是随着研究深入，出现稀疏数据成像问题，无法用传统方法重建清晰图像。
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