A portion of a software product is analyzed to determine a language and a subject-matter domain of the portion. A string is extracted from the portion, where the string has been translated into the language from an original string in an original language in a version of the software product. A corpus including a set of stored strings in the first language is selected. A subset of stored strings is selected from a content that is related to the subject-matter domain of the software product. When the string matches a stored string in the corpus, the string is selected into a shortlist and when a second string extracted from the portion fails to match any stored string in the corpus, the second string is excluded from the shortlist. The shortlist is output, causing a review of an accuracy of a machine translation process to be performed.

In an aspect of the present disclosure, a method for analyzing the speech delivery of a user is disclosed including presenting to the user a plurality of speech delivery analysis criteria, receiving from the user a selection of at least one of the speech delivery analysis criterion, receiving, from at least one sensing device, speech data captured by the at least one sensing device during the delivery of a speech by the user, transmitting the speech data and the selected at least one speech delivery analysis criterion to an analysis engine for analysis based on the selected at least one speech delivery analysis criteria, receiving, from the analysis engine an analysis report for the speech data, the analysis report comprising an analysis of the speech data performed by the analysis engine based on the selected at least one criterion, and presenting to the user the analysis report.

Some examples include generating a personal language model based on linguistic characteristics of one or more files stored at one or more locations in a file system. Further, some implementations include predicting and presenting a non-Latin character string based at least in part on the personal language model, such as in response to receiving a Latin character string via an input method editor interface.

A method, system and computer program product for generating an optimization model. A prescriptive model including a structured set of data and instance(s) of templates of a technical and/or business prescriptive domain is received, where the instances of the templates define goals and/or constraints for the structured set of data. The business prescriptive domain extends the technical prescriptive domain defining a generic optimization problem and the business prescriptive domain defines a more specific business problem. The prescriptive model is translated into a technical prescriptive model using a set of mapping rules to translate the instance(s) of the templates of the business prescriptive domain into instance(s) of the templates of the technical prescriptive domain from which the business prescriptive domain extends. Furthermore, the technical prescriptive model is translated into an optimization model, where the optimization model is solved. An output defining a solution from the solved optimization model is then presented.

The systems and techniques described herein implement an improved gaze-based on-screen keyboard that provide dynamically variable dwell times to increase throughput and reduce errors. Utilizing a language model, the probability that each key of the on-screen keyboard will be the subsequential key can be determined, and based at least in part on this determined probability, a dwell time can be assigned to each key. When used as an iterative process, a minimum dwell time may be gradually reduced as confidence in the subsequential key increases to provide a cascading minimum dwell time.

A condition determining section (24) determines whether or not two consecutive lines in an image meet a joining condition that is based on a characteristic of a language of a character string, the two consecutive lines being extracted from the character string composed of a plurality of lines. In a case where the joining condition is met, an extracted line joining section (25) and a translation section (26) join and then translate the two consecutive lines.

A translation display device of the present invention carries out a translation process of translating text extracted from a certain image and displays translated text while a display frame rate is maintained. A translation processing section (5) carries out the translation process in a case where no image other than the certain image is being subjected to the translation process. An image movement analyzing section (7) identifies a displacement of a position of an object in a most recent image which has been most recently obtained, the displacement being measured with respect to a reference position of an object in a reference image for which the translation process most recently ended. A generated image in which translated text obtained by translating extracted text extracted from the reference image is displayed so as to be superimposed on the most recent image in accordance with (i) a position of the extracted text and (ii) information on the displacement.

Systems and methods for evaluating text classifier parameters based on semantic features. An example method comprises: performing a semantico-syntactic analysis of a natural language text of a corpus of natural language texts to produce a semantic structure representing a set of semantic classes; identifying a natural language text feature to be extracted using a set of values of a plurality of feature extraction parameters; partitioning the corpus of natural language texts into a training data set comprising a first plurality of natural language texts and a validation data set comprising a second plurality of natural language texts; determining, in view of the category of the training data set, the set of values of the feature extraction parameters; validating the set of values of the feature extraction parameters using the validation data set.

Techniques to response to respond to user requests using natural-language machine learning based on branching example conversations are described. In one embodiment, an apparatus may comprise a bot application interface component operative to receive an example-interaction repository, the example-interaction repository comprising a plurality of example user-to-bot interactions, including one or more branching example user-to-bot interactions; and an interaction processing component operative to generate a linearized example-interaction repository by replacing the one or more branching example user-to-bot interactions with the plurality of linearized example user-to-bot interactions; submit the example-interaction repository to a natural-language machine learning component; and receive a sequence model from the natural-language machine learning component in response to submitting the example-interaction repository; and a client communication component operative to perform a user-to-bot conversation based on the sequence model. Other embodiments are described and claimed.

A method and device are provided for translating object information and acquiring derivative information, including obtaining, based on the acquired source-object information, target-object information corresponding to the source object by translation, and outputting the target-object information. A language environment corresponding to the source object is different from a language environment corresponding to the target object. By applying the present disclosure, the range of machine translation subjects can be expanded, and the applicability of translation can be enhanced, a user's requirements on translation of objects can be met.