Abstract: A computer-implemented technique is described herein for assisting a user in advancing a task objective. The technique uses a suggestion-generating system (SGS) to provide one or more suggestions to a user in response to at least a last-submitted query provided by the user. The SGS may correspond to a classification-type or generative-type neural network. The SGS uses a machine-trained model that is trained using a multi-task training framework based on plural groups of training examples, which, in turn, are produced using different respective example-generating methods. One such example-generating method constructs a training example from queries in a search session. It operates by identifying the task-related intent the queries, and then identifying at least one sequence of queries in the search session that exhibits a coherent task-related intent. A training example is constructed based on queries in such a sequence.

Abstract: A computer-implemented technique is described herein for assisting a user in advancing a task objective. The technique uses a suggestion-generating system (SGS) to provide one or more suggestions to a user in response to at least a last-submitted query provided by the user. The SGS may correspond to a classification-type or generative-type neural network. The SGS uses a machine-trained model that is trained using a multi-task training framework based on plural groups of training examples, which, in turn, are produced using different respective example-generating methods. One such example-generating method constructs a training example from queries in a search session. It operates by identifying the task-related intent the queries, and then identifying at least one sequence of queries in the search session that exhibits a coherent task-related intent. A training example is constructed based on queries in such a sequence.

Abstract: Described herein is a mechanism for utilizing a neural network to identify and rank search results. A machine learning model is trained by converting training data comprising query-document entries into query term-document entries. The query term-document entries are utilized to train the machine learning model. A set of query terms are identified. The query terms can be derived from a query history. The trained machine learning model is used to calculate document ranking scores for the query terms and the resultant scores are stored in a pre-calculated term-document index. A query to search the document index is broken down into its constituent terms and an aggregate document ranking score is calculated from a weighted sum of the document ranking scores corresponding to the individual query terms. Because the term-document index can be pre-calculated, it can be downloaded to provide deep learning search capabilities in a computationally limited environment.

Abstract: Described herein is a mechanism for utilizing a neural network to identify and rank search results. A machine learning model is trained by converting training data comprising query-document entries into query term-document entries. The query term-document entries are utilized to train the machine learning model. A set of query terms are identified. The query terms can be derived from a query history. The trained machine learning model is used to calculate document ranking scores for the query terms and the resultant scores are stored in a pre-calculated term-document index. A query to search the document index is broken down into its constituent terms and an aggregate document ranking score is calculated from a weighted sum of the document ranking scores corresponding to the individual query terms. Because the term-document index can be pre-calculated, it can be downloaded to provide deep learning search capabilities in a computationally limited environment.

Abstract: A computer-implemented technique is described herein for assisting a user in advancing a task objective. The technique uses a suggestion-generating system (SGS) to provide one or more suggestions to a user in response to at least a last-submitted query provided by the user. The SGS may correspond to a classification-type or generative-type neural network. The SGS uses a machine-trained model that is trained using a multi-task training framework based on plural groups of training examples, which, in turn, are produced using different respective example-generating methods. One such example-generating method constructs a training example from queries in a search session. It operates by identifying the task-related intent the queries, and then identifying at least one sequence of queries in the search session that exhibits a coherent task-related intent. A training example is constructed based on queries in such a sequence.

Abstract: A computer-implemented technique receives an input expression that a user submits with an intent to accomplish some objective. The technique then uses a machine-trained intent encoder component to map the input expression into an input expression intent vector (IEIV). The IEIV corresponds to a distributed representation of the intent associated with the input expression, within a vector intent vector space. The technique then leverages the intent vector to facilitate some downstream application task, such as the retrieval of information. Some application tasks also use a neighbor search component to find expressions that express an intent similar to that of the input expression. A training system trains the intent encoder component based on the nexus between queries and user clicks, as recorded in a search engine's search log.

Abstract: Described herein is a mechanism for utilizing a neural network to identify and rank search results. A machine learning model is trained by converting training data comprising query-document entries into query term-document entries. The query term-document entries are utilized to train the machine learning model. A set of query terms are identified. The query terms can be derived from a query history. The trained machine learning model is used to calculate document ranking scores for the query terms and the resultant scores are stored in a pre-calculated term-document index. A query to search the document index is broken down into its constituent terms and an aggregate document ranking score is calculated from a weighted sum of the document ranking scores corresponding to the individual query terms. Because the term-document index can be pre-calculated, it can be downloaded to provide deep learning search capabilities in a computationally limited environment.

Abstract: A computer-implemented technique is described herein that receives an input expression that a user submits with an intent to accomplish some objective. The technique then uses a machine-trained intent encoder component to map the input expression into an input expression intent vector (IEIV). The IEIV corresponds to a distributed representation of the intent associated with the input expression, within a vector intent vector space. The technique then leverages the intent vector to facilitate some downstream application task, such as the retrieval of information. Some application tasks also use a neighbor search component to find expressions that express an intent similar to that of the input expression. A training system trains the intent encoder component based on the nexus between queries and user clicks, as recorded in a search engine's search log.

Abstract: Non-limiting examples of the present disclosure describe low latency pre-web classification of query data. In examples, processing is performed where query data may be analyzed in a low latency manner that includes providing a vertical intent classification and entity identification for query data before a web ranking service processes the query data. Query data may be received. A vertical intent classification index may be searched using the query data. In examples, the vertical intent classification index may comprise a set of files that can be used to determine one or more candidate entity identifiers for the query data. The one or more entity identifiers may be ranked. The query data, a vertical intent classification for the vertical intent classification index and the one or more ranked candidate entity identifiers may be transmitted for processing associated with a web ranking service. Other examples are also described.

Abstract: Systems, methods, and computer-readable storage media are provided for permitting users to search the content of a plurality of apps from a single search query input location. A user inputs a search query and a plurality of apps and/or the content thereof is searched to determine relevancy to the input query. If an app having relevant app content is identified, it may be determined if the app is associated with the querying user. If it is determined that the app is associated with the querying user, the information determined relevant to the input query may be presented to the user. If, it is determined that the app is not associated with the user, the user may be presented with the identity of the app and/or be directed to a location (e.g., an app store) where the user can become associated with the identified app and obtain the desired information.

Abstract: Non-limiting examples of the present disclosure describe low latency pre-web classification of query data. In examples, processing is performed where query data may be analyzed in a low latency manner that includes providing a vertical intent classification and entity identification for query data before a web ranking service processes the query data. Query data may be received. A vertical intent classification index may be searched using the query data. In examples, the vertical intent classification index may comprise a set of files that can be used to determine one or more candidate entity identifiers for the query data. The one or more entity identifiers may be ranked. The query data, a vertical intent classification for the vertical intent classification index and the one or more ranked candidate entity identifiers may be transmitted for processing associated with a web ranking service. Other examples are also described.

Abstract: Embodiment described herein are directed to an enhanced search engine with multiple feedback loops for providing optimal search results that are responsive a user's search query. The user's search query is parsed, and based on the underlying terms, different linguistic models and refinement techniques generate alternative candidate search queries that may yield better results. Searches are performed for the original search query and the candidate search queries, and different scores are used to select the best search results to present to the user. Results making it onto the list, as well as the underlying candidate search query, linguistic model, or refinement technique for generating that search query, will have their corresponding scores updated to reflect their success of generating a search result. Scores are stored and used by future searches to come up with better results.

Abstract: Systems, methods, and computer-readable storage media are provided for permitting users to search the content of a plurality of apps from a single search query input location. A user inputs a search query and a plurality of apps and/or the content thereof is searched to determine relevancy to the input query. If an app having relevant app content is identified, it may be determined if the app is associated with the querying user. If it is determined that the app is associated with the querying user, the information determined relevant to the input query may be presented to the user. If, it is determined that the app is not associated with the user, the user may be presented with the identity of the app and/or be directed to a location (e.g., an app store) where the user can become associated with the identified app and obtain the desired information.

Abstract: Subject matter described herein is related to determining a document score, which suggests a relevance of a document (e.g., webpage) to a search query. For example, a search query is received that is comprised of one or more terms, which represent a subject. An equivalent subject is identified that is semantically similar to the subject. The document score is determined by accounting for both a subject frequency and an equivalent-subject frequency.

Abstract: Computer-readable media and computerized methods for identifying candidate points on a graphical depiction of relative popularity of an entity (e.g., entertainer, sports team, and the like) are provided. Points on the graphical depiction are ranked based on a number of user-submitted web queries that reference the entity that are received during a particular time frame. Peak points and slope values (i.e., derived from an angle of inclination of inclines on the graphical depiction) may be captured by analyzing movements in the rank of an entity over time. An algorithmic process may then be applied to the peak points and slope values to determine points of interest of the entity's popularity, such as the highest-ranked periods and/or dramatic positive movements in rank of the entity. These points of interest are selected as candidate points and are surfaced as icons on a visual representation of the graphical depiction.

Abstract: Embodiment described herein are directed to an enhanced search engine with multiple feedback loops for providing optimal search results that are responsive a user's search query. The user's search query is parsed, and based on the underlying terms, different linguistic models and refinement techniques generate alternative candidate search queries that may yield better results. Searches are performed for the original search query and the candidate search queries, and different scores are used to select the best search results to present to the user. Results making it onto the list, as well as the underlying candidate search query, linguistic model, or refinement technique for generating that search query, will have their corresponding scores updated to reflect their success of generating a search result. Scores are stored and used by future searches to come up with better results.

Abstract: Methods, systems, and media are provided for generating one or more spelling candidates. A query log is received, which contains one or more user-input queries. The user-input queries are divided into one or more common context groups. Each term of the user-input queries is ranked within a common context group according to a frequency of occurrence to form a ranked list for each of the one or more common context groups. A chain algorithm is implemented to the respective ranked lists to identify a base word and a set of one or more subordinate words paired with the base word. The base word and all sets of the subordinate words from all of the respective ranked lists are aggregated to form one or more chains of spelling candidates for the base word.

Abstract: A computer system, method, and media for associating locations with ranked websites are provided. The computer system includes a search engine, a log database, and a location database that are employed to respond to search requests from users by returning appropriately ranked websites to the user. The websites are ranked using the location of the website and the location of the user to select websites to receive high ranks. Additionally, the search engine includes a correction feature that reevaluates locations for a website or user when a large number of obtained locations suggest a different location than a currently associated location for the website or the user.

Abstract: A computer system, method, and media for associating locations with ranked websites are provided. The computer system includes a search engine, a log database, and a location database that are employed to respond to search requests from users by returning appropriately ranked websites to the user. The websites are ranked using the location of the website and the location of the user to select websites to receive high ranks. Additionally, the search engine includes a correction feature that reevaluates locations for a website or user when a large number of obtained locations suggest a different location than a currently associated location for the website or the user.

Abstract: An approach is provided for determining related queries for a given search query based on the linking structure of electronic documents within a document set. Document titles are used to represent potential search queries and links between the electronic documents are used to determine relationships between the potential search queries. As such, the document set may be represented as a directed graph in which document titles (which represent potential search queries) are nodes and links are edges between the nodes. When a particular search query is received, a corresponding node is identified and related queries are determined by identifying other nodes having connections with that node.