We should take into consideration the data sparseness problem because the data do not contain all phenomena of the language in most cases.
The girth as another graph parameter controlling the sparseness of a graph is less well studied in this context.
This sparseness limits the communication necessary through the constraint network, and permits effective distribution of work among agents.
Note that, due to data sparseness, all of these words, though related with soccer have less probability when compared to stem-based and noun-based models.
To do this, we make use of some properties of sparse matrices since nearly all economic models display sparseness.
Traditional attempts to tackle the problem of data sparseness include the class-based approaches and similarity-based approaches.
We surmise that these constraints simply differ from corpus to corpus and are related to general data sparseness effects.
Thus, one would expect the variance in the estimated responses to grow with increasing sparseness.
