Abstract: Systems, methods, and computer programming products for alleviating ambiguity amongst the terms and language displayed by the user interface of software products and services. The disclosed solutions catalog terms displayed by the UI of software and services and identify where overlapping terms with the same or substantially similar term names are presented by the UI but have different meanings than the software most familiar to the user. Natural language processing is leveraged to derive meanings of software terms using the context of the surrounding words and text elements within the UI, as well as product documentation, error messages, sentiment and other textual clues. Ambiguity among overlapping terms is alleviated by modifying the UI, highlighting differences in term definitions from the software or services a user is most familiar with using, and updating the UI in a manner that differentiates the overlapping terms displayed by accessed products or services.

Abstract: A method of statistical machine translation (SMT) is provided. The method comprises generating reordering knowledge based on the syntax of a source language (SL) and a number of alignment matrices that map sample SL sentences with sample target language (TL) sentences. The method further comprises receiving a SL word string and parsing the SL word string into a parse tree that represents the syntactic properties of the SL word string. The nodes on the parse tree are reordered based on the generated reordering knowledge in order to provide reordered word strings. The method further comprises translating a number of reordered word strings to create a number of TL word strings, and identifying a statistically preferred TL word string as a preferred translation of the SL word string.

Abstract: A method is disclosed to synchronously generate from a single stenographic input two or more streaming text outputs each comprising a different language. The method provides a stenographic data stream comprising a plurality of first language-based encoded words, and synchronously forms a first language streaming text output and a second language streaming text output.

Abstract: Classification of sequences, such as the translation of natural language sentences, is carried out using an independence assumption. The independence assumption is an assumption that the probability of a correct translation of a source sentence word into a particular target sentence word is independent of the translation of other words in the sentence. Although this assumption is not a correct one, a high level of word translation accuracy is nonetheless achieved. In particular, discriminative training is used to develop models for each target vocabulary word based on a set of features of the corresponding source word in training sentences, with at least one of those features relating to the context of the source word. Each model comprises a weight vector for the corresponding target vocabulary word.