Abstract: A method for assisting a user to connect a problem with a device, such as a printer includes extracting, from records comprising user actions on the device, string of user actions on the device. The string of user action is compared with at least one predetermined sequence of user actions for correction of predefined problem with the device. Based on the comparison, an evaluation is made as to whether at least one prior user has attempted the predetermined sequence and, if so, a procedure is implemented to avoid a user repeating the prior attempt.

Abstract: Quality of machine translation of natural language is determined by computing a sequence kernel that provides a measure of similarity between a first sequence of symbols representing a machine translation in a target natural language and a second sequence of symbols representing a reference translation in the target natural language. The measure of similarity takes into account the existence of non-contiguous subsequences shared by the first sequence of symbols and the second sequence of symbols. When the similarity measure does not meet an acceptable threshold level, the translation model of the machine translator may be adjusted to improve subsequent translations performed by the machine translator.

Abstract: A computer implemented method and an apparatus for comparing spans of text are disclosed. The method includes computing a similarity measure between a first sequence of symbols representing a first text span and a second sequence of symbols representing a second text span as a function of the occurrences of optionally noncontiguous subsequences of symbols shared by the two sequences of symbols. Each of the symbols comprises at least one consecutive word and is defined according to a set of linguistic factors. Pairs of symbols in the first and second sequences that form a shared subsequence of symbols are each matched according to at least one of the factors.

Abstract: A machine translation method includes receiving source text in a first language and retrieving text fragments in a target language from a library of bi-fragments to generate a target hypothesis. Each bi-fragment includes a text fragment from the first language and a corresponding text fragment from the second language. Some of the bi-fragments are modeled as elastic bi-fragments where a gap between words is able to assume a variable size corresponding to a number of other words to occupy the gap. The target hypothesis is evaluated with a translation scoring function which scores the target hypothesis according to a plurality of feature functions, at least one of the feature functions comprising a gap size scoring feature which favors hypotheses with statistically more probable gap sizes over hypotheses with statically less probable gap sizes.

Abstract: A machine translation method for translating source text from a first language to target text in a second language includes receiving the source text in the first language and accessing a library of bi-fragments, each of the bi-fragments including a text fragment from the first language and a text fragment from the second language, at least some of the bi-fragments comprising non-contiguous bi-fragments in which at least one of the text fragment from the first language and the text fragment from the second language comprises a non-contiguous fragment.

Abstract: An alignment method includes, for a source sentence in a source language, identifying whether the sentence includes at least one candidate term comprising a contiguous subsequence of words of the source sentence. A target sentence in a target language is aligned with the source sentence. This includes developing a probabilistic model which models conditional probability distributions for alignments between words of the source sentence and words of the target sentence and generating an optimal alignment based on the probabilistic model, including, where the source sentence includes the at least one candidate term, enforcing a contiguity constraint which requires that all the words of the target sentence which are aligned with an identified candidate term form a contiguous subsequence of the target sentence.

Abstract: A machine translation method includes receiving source text in a first language and retrieving text fragments in a target language from a library of bi-fragments to generate a target hypothesis. Each bi-fragment includes a text fragment from the first language and a corresponding text fragment from the second language. Some of the bi-fragments are modeled as elastic bi-fragments where a gap between words is able to assume a variable size corresponding to a number of other words to occupy the gap. The target hypothesis is evaluated with a translation scoring function which scores the target hypothesis according to a plurality of feature functions, at least one of the feature functions comprising a gap size scoring feature which favors hypotheses with statistically more probable gap sizes over hypotheses with statically less probable gap sizes.

Abstract: A method for assisting a user to correct a problem with a device, such as a printer includes extracting, from records comprising user actions on the device, a string of user actions on the device. The string of user actions is compared with at least one predetermined sequence of user actions for correction of a predefined problem with the device. Based on the comparison, an evaluation is made as to whether at least one prior user has attempted the predetermined sequence and, if so, a procedure is implemented to avoid a user repeating the prior attempt.

Abstract: A machine translation method for translating source text from a first language to target text in a second language includes receiving the source text in the first language and accessing a library of bi-fragments, each of the bi-fragments including a text fragment from the first language and a text fragment from the second language, at least some of the bi-fragments comprising non-contiguous bi-fragments in which at least one of the text fragment from the first language and the text fragment from the second language comprises a non-contiguous fragment.

Abstract: A measure of similarity between a first sequence of symbols and a second sequence of symbols is computed. Memory is allocated for a computational unit for storing values that are computed using a recursive formulation that computes the measure of similarity based on matching subsequences of symbols between the first sequence of symbols and the second sequence of symbols. A processor computes for the computational unit the values for the measure of similarity using the recursive formulation within which functions are computed using nested loops. The measure of similarity is output by the computational unit to an information processing application.

Abstract: Quality of machine translation of natural language is determined by computing a sequence kernel that provides a measure of similarity between a first sequence of symbols representing a machine translation in a target natural language and a second sequence of symbols representing a reference translation in the target natural language. The measure of similarity takes into account the existence of non-contiguous subsequences shared by the first sequence of symbols and the second sequence of symbols. When the similarity measure does not meet an acceptable threshold level, the translation model of the machine translator may be adjusted to improve subsequent translations performed by the machine translator.

Abstract: A measure of similarity between a first sequence of symbols and a second sequence of symbols is computed. Memory is allocated for a computational unit for storing values that are computed using a recursive formulation that computes the measure of similarity based on matching subsequences of symbols between the first sequence of symbols and the second sequence of symbols. A processor computes for the computational unit the values for the measure of similarity using the recursive formulation within which functions are computed using nested loops. The measure of similarity is output by the computational unit to an information processing application.