Abstract: A computer-implemented technique is described herein for modifying original keyword information to increase the probability that it will match the queries input by users. The technique operates by using a search engine to provide supplemental information that is relevant to the original keyword information. The technique then mines the supplemental information to extract frequently-occurring n-grams. Next, the technique removes n-grams that are considered to represent noise, and then uses a deep-structured machine-learned model to assign score values to the remaining n-grams. Finally, the technique supplements and/or replaces the original keyword information with the highest-scoring n-grams.

Abstract: A help interface is generated to present static help content and customized help content for an application. Help content data is accessed to define the static help content and a control command to support incorporation of the customized help content. The control command is implemented to determine a configuration of the application, and a user context is determined based on a user characteristic and the application configuration. The customized help content is then resolved based on the control command and the user context. In some cases, a help system may include a help viewer module or program in communication with a computer-readable memory storing the help content data. The help viewer may then render the help interface based on the static help content and customized help content resolved from the control command, the application configuration, and other aspects of the user context in which it is presented.

Abstract: Functionality is described herein for analyzing an input linguistic item, such as a query, in a series of stages. The linguistic item includes one or more candidate items. In a first stage, a brand classifier component determines whether the linguistic item specifies at least one brand, to provide a classifier output result. In a second stage, a tagging component generates a set of tags for at least some of the candidate items in the linguistic item, based, in part, on the classifier output result, to generate a tagging output result. An action-taking component then generates at least one result item based on the tagging output result. Functionality is also described herein for producing the brand classifier component and the tagging component using machine-learning training techniques. The training techniques may include provisions to address the later appearance of new brands that do not appear in a brand dictionary.

Abstract: A computer-implemented technique is described herein for modifying original keyword information to increase the probability that it will match the queries input by users. The technique operates by using a search engine to provide supplemental information that is relevant to the original keyword information. The technique then mines the supplemental information to extract frequently-occurring n-grams. Next, the technique removes n-grams that are considered to represent noise, and then uses a deep-structured machine-learned model to assign score values to the remaining n-grams. Finally, the technique supplements and/or replaces the original keyword information with the highest-scoring n-grams.

Abstract: Functionality is described herein for analyzing an input linguistic item, such as a query, in a series of stages. The linguistic item includes one or more candidate items. In a first stage, a brand classifier component determines whether the linguistic item specifies at least one brand, to provide a classifier output result. In a second stage, a tagging component generates a set of tags for at least some of the candidate items in the linguistic item, based, in part, on the classifier output result, to generate a tagging output result. An action-taking component then generates at least one result item based on the tagging output result. Functionality is also described herein for producing the brand classifier component and the tagging component using machine-learning training techniques. The training techniques may include provisions to address the later appearance of new brands that do not appear in a brand dictionary.

Abstract: A method and system is disclosed for generating a help interface to present static help content and customized help content for an application. Help content data is accessed to define the static help content and a control command to support incorporation of the customized help content. The control command is implemented to determine a configuration of the application, and a user context is determined based on a user characteristic and the application configuration. The customized help content is then resolved based on the control command and the user context. In some cases, a help system may include a help viewer module or program in communication with a computer-readable memory storing the help content data. The help viewer may then render the help interface based on the static help content and customized help content resolved from the control command, the application configuration, and other aspects of the user context in which it is presented.