Abstract: Examples of the present disclosure describe systems and methods of providing sequential two-handed touch typing. In aspects, a client device may provide a touch-based input receiving application. The client device may include one or more sensors operable to detect the approach of a user's hand or touch-based tool. In response to a detection, the client device may determine input interaction information, such as aspects of the hand (e.g., right or left) and/or finger(s) being used. The input interaction information may be used to detect candidate keys and to provide a keyboard (or a portion of a keyboard) based thereon. In aspects, the provided keyboard or keyboard portion may be altered in response to a subsequent detection.

Abstract: Examples of the present disclosure describe systems and methods of providing sequential two-handed touch typing. In aspects, a client device may provide a touch-based input receiving application. The client device may include one or more sensors operable to detect the approach of a user's hand or touch-based tool. In response to a detection, the client device may determine input interaction information, such as aspects of the hand (e.g., right or left) and/or finger(s) being used. The input interaction information may be used to detect candidate keys and to provide a keyboard (or a portion of a keyboard) based thereon. In aspects, the provided keyboard or keyboard portion may be altered in response to a subsequent detection.

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: Examples of the present disclosure describe systems and methods of providing sequential two-handed touch typing. In aspects, a client device may provide a touch-based input receiving application. The client device may include one or more sensors operable to detect the approach of a user's hand or touch-based tool. In response to a detection, the client device may determine input interaction information, such as aspects of the hand (e.g., right or left) and/or finger(s) being used. The input interaction information may be used to detect candidate keys and to provide a keyboard (or a portion of a keyboard) based thereon. In aspects, the provided keyboard or keyboard portion may be altered in response to a subsequent detection.

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 media for identifying related strings for search query rewriting are provided. Session data for a user search query session in an accessed click log data is identified. It is determined whether a first additional search query in the session data is related to a first user search query based on at least one of: dwell time; a number of search result links clicked on; and similarity between web page titles or uniform resource locators (URLs). When related, the first additional search query is incorporated into a list of strings related to the first user search query. One or more supplemental strings that are related to the first user search query are also identified. The identified supplemental strings are also included in the list of strings related to the first user search query.

Abstract: Systems, methods, and computer media for identifying query rewriting replacement terms are provided. A list of related string pairs each comprising a first string and second string is received. The first string of each related string pair is a user search query extracted from user click log data. For one or more of the related string pairs, the string pair is provided as inputs to a statistical machine translation model. The model identifies one or more pairs of corresponding terms, each pair of corresponding terms including a first term from the first string and a second term from the second string. The model also calculates a probability of relatedness for each of the one or more pairs of corresponding terms. Term pairs whose calculated probability of relatedness exceeds a threshold are characterized as query term replacements and incorporated, along with the probability of relatedness, into a query rewriting candidate database.

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: Methods, systems, and computer-readable media for suggesting search queries to a user while the user is typing in characters of a search query. The suggested search queries are based in part on the characters entered and are presented before the user submits the query to the search engine. The suggested queries may be displayed in a dropdown box that allows the user to select one of the suggested queries. Embodiments of the present invention present search queries that do not begin with the characters entered by the user.

Abstract: Systems, methods, and computer media for identifying related strings for search query rewriting are provided. Session data for a user search query session in an accessed click log data is identified. It is determined whether a first additional search query in the session data is related to a first user search query based on at least one of: dwell time; a number of search result links clicked on; and similarity between web page titles or uniform resource locators (URLs). When related, the first additional search query is incorporated into a list of strings related to the first user search query. One or more supplemental strings that are related to the first user search query are also identified. The identified supplemental strings are also included in the list of strings related to the first user search query.

Abstract: Systems, methods, and computer media for identifying query rewriting replacement terms are provided. A list of related string pairs each comprising a first string and second string is received. The first string of each related string pair is a user search query extracted from user click log data. For one or more of the related string pairs, the string pair is provided as inputs to a statistical machine translation model. The model identifies one or more pairs of corresponding terms, each pair of corresponding terms including a first term from the first string and a second term from the second string. The model also calculates a probability of relatedness for each of the one or more pairs of corresponding terms. Term pairs whose calculated probability of relatedness exceeds a threshold are characterized as query term replacements and incorporated, along with the probability of relatedness, into a query rewriting candidate database.