.g., Brown versus Wall Street Journal), and n-gram order (bigram versus trigram) affect the relative performance of these methods, which we measure through the cross-entropy of test data. In addition, we introduce two novel smoothing techniques, one a variation of Jelinek-Mercer smoothing and one a very

We introduce a method for solving substitution ciphers using low-order letter n-gram models. This method enforces global constraints using integer programming, and it guarantees that no decipherment key is overlooked. We carry out extensive empirical experiments showing how decipherment accuracy

term-distance and term-occurrence information for improving

out-of-vocabulary words using multi class-based

Human evaluations of machine translation are extensive but expensive. Human evaluations can take months to finish and involve human labor that can not be reused.

of the longest common subsequence. We develop a notion of n-gram similarity and distance. We show that edit distance and the length of the longest common subsequence are special cases of n-gram distance and similarity, respectively. We provide formal, recursive definitions of n-gram similarity and distance

range of parameters, even when the array is perfectly known to the informed beamformer. The key assumption blind identification relies on is the statistical independence of the sources, which we exploit using fourth-order cumulants. A computationally efficient technique is presented for the blind

models suggests that they may be an advantageous alternative, both theoretically and practically, to the widely used n-gram models.

of mutual independence between the signals. The weakness of the assumptions makes it a powerful approach but requires to venture beyond familiar second order statistics. The objective of this paper is to review some of the approaches that have been recently developed to address this exciting problem

, lexical methods are necessarily incomplete and imprecise. Using the singular value decomposition (SVD), one can take advantage of the implicit higher-order structure in the association of terms with documents by determining the SVD of large sparse term by document matrices. Terms and documents represented