Abstract: Described herein is a screen reader system for providing contextually relevant information for ambiguous link(s). Input is received from a user requesting information about a particular link of a web page. In response to the received user input, a request for contextually relevant information regarding the particular link (e.g., page identifier) is provided to a search engine. The request can include a uniform resource locator (URL) associated with the particular link. Contextually relevant information regarding the particular link is received from the search engine. The received contextually relevant information is provided (e.g., displayed and/or read) to the user in response to the received request for information.

Abstract: Described herein is a screen reader providing a summary with popular link(s). The screen reader can provide a request for popular link information regarding a particular web resource to a search engine. The request can include a URL associated with the particular web resource. The request can be provided in response to a user request for a summary and/or the request can be provided based upon a user configurable setting (e.g., a quantity of popular links to be displayed). The screen reader can receive popular link information regarding the particular web resource from the search engine. The screen reader can then provide a summary comprising at least some of the received popular link information to the user. In response to selection of a particular popular link, the screen reader can redirect the browser to display a web resource associated with the particular popular link.

Abstract: Described herein is a screen reader providing a summary with popular link(s). The screen reader can provide a request for popular link information regarding a particular web resource to a search engine. The request can include a URL associated with the particular web resource. The request can be provided in response to a user request for a summary and/or the request can be provided based upon a user configurable setting (e.g., a quantity of popular links to be displayed). The screen reader can receive popular link information regarding the particular web resource from the search engine. The screen reader can then provide a summary comprising at least some of the received popular link information to the user. In response to selection of a particular popular link, the screen reader can redirect the browser to display a web resource associated with the particular popular link.

Abstract: Described herein is a screen reader system for providing contextually relevant information for ambiguous link(s). Input is received from a user requesting information about a particular link of a web page. In response to the received user input, a request for contextually relevant information regarding the particular link (e.g., page identifier) is provided to a search engine. The request can include a uniform resource locator (URL) associated with the particular link. Contextually relevant information regarding the particular link is received from the search engine. The received contextually relevant information is provided (e.g., displayed and/or read) to the user in response to the received request for information.

Abstract: Architecture that utilizes server-based signals (e.g., past engagement, application popularity, spell-correction, mined search patterns, machine learning models, etc.) to improve relevance of search results for local applications and settings. The architecture works for any operating system (OS) and any client device that has local settings or applications installed. The architecture also covers instances where server-signals are being used to improve queries on devices where settings are searched but no applications are installed or will not be installed.

Abstract: Described is a technology for identifying sample data items (e.g., documents corresponding to query-URL pairs) having the greatest likelihood of being mislabeled when previously judged, and selecting those data items for re-judging. In one aspect, lambda gradient scores (information associated with ranked sample data items that indicates a relative direction and how “strongly” to move each data item for lowering a ranking cost) are summed for pairs of sample data items to compute re-judgment scores for each of those sample data items. The re-judgment scores indicate a relative likelihood of mislabeling. Once the selected sample data items are re-judged, a new training set is available, whereby a new ranker may be trained.

Abstract: A search management system receives at a computing system one or more search results from a search operation based on a search query. The search operation performed a first search on content in a database accessible through an application, a second search on local to the computing system content, and a third search on Web content. At least one of the search results from the first search is associated with an application identifier identifying the application. A search results integrator ranks the search results from the first search, the second search, and the third search in aggregation according to historical search behavior collected about a user and presents the ranked search results from the first search, the second search, and the third search in an integrated view.

Abstract: Systems and methods for clarifying a computer user's intent with regard to one or more query terms of a search query are presented. Often, one or more query terms of a search query are ambiguous and/or unclear. As presented herein, upon identifying a query term in the search query that is unclear, clarification data is obtained and presented to the computer user by way of a clarification user interface. The computer user is able to identify one or more aspects of the computer user's intent, intent clarifications, with regard to the query term through the clarification user interface. Upon instruction by the computer user, the search query, along with the intent clarifications, is submitted to a search engine and the search results obtained from the search engine are presented to the computer user.

Abstract: Model adaptation may be performed to take a general model trained with a set of training data (possibly large), and adapt the model using a set of domain-specific training data (possibly small). The parameters, structure, or configuration of a model trained in one domain (called the background domain) may be adapted to a different domain (called the adaptation domain), for which there may be a limited amount of training data. The adaption may be performed using the Boosting Algorithm to select an optimal basis function that optimizes a measure of error of the model as it is being iteratively refined, i.e., adapted.

Abstract: Described is a technology for identifying sample data items (e.g., documents corresponding to query-URL pairs) having the greatest likelihood of being mislabeled when previously judged, and selecting those data items for re-judging. In one aspect, lambda gradient scores (information associated with ranked sample data items that indicates a relative direction and how “strongly” to move each data item for lowering a ranking cost) are summed for pairs of sample data items to compute re-judgment scores for each of those sample data items. The re-judgment scores indicate a relative likelihood of mislabeling. Once the selected sample data items are re-judged, a new training set is available, whereby a new ranker may be trained.

Abstract: Model adaptation may be performed to take a general model trained with a set of training data (possibly large), and adapt the model using a set of domain-specific training data (possibly small). The parameters, structure, or configuration of a model trained in one domain (called the background domain) may be adapted to a different domain (called the adaptation domain), for which there may be a limited amount of training data. The adaption may be performed using the Boosting Algorithm to select an optimal basis function that optimizes a measure of error of the model as it is being iteratively refined, i.e., adapted.