This sparseness is actually a factor throughout the book, despite the practical detail in the case studies.
The sparseness of food resources kept population densities quite low prior to the development of agriculture.
This is justified by the fact that sparseness is far more severe for lexical probabilities than for language model probabilities.
This is a typical result of the data sparseness we have encountered while training the word-based model.
We can overcome the problem of data sparseness by applying not co-occurring words but co-occurring clusters to the similarity of target words.
When we consider the words with different inflectional and derivational suffixes different, then we have to deal with data sparseness.
Some influence of temporal sparseness upon the strength of the feedback would be required in that case.
Thus, one would expect the variance in the estimated responses to grow with increasing sparseness.
