Co-occurrence smoothing for stochastic language modeling . U. Essen and V. Steinbiss. Cooccurrence smoothing for stochastic language modeling . The advantage of the N gram model is that it captures all the information provided by the preceding N Gamma 1 words. Judging from its success, this is quite an important source of information, especially for fixed word order languages like English.
LOB corpus is an adequate choice.
This is common practice, mainly for practical reasons. Cooccurrence smoothing for stochastic language modeling . In information retrieval, there have been essentially two proposals to overcome the sparseness problem. U. Essen and V. Steinbiss. Cooccurrence smoothing for stochastic language modeling .