Abstract: The present disclosure relates to processing operations configured to uniquely utilize indexing of content to improve content retrieval processing, particularly when working with large data sets. The techniques described herein enables efficient content retrieval when working with large data sets such as those that may be associated with a plurality of tenants of a data storage application/service. Among other technical advantages, the present disclosure is applicable to train a classifier using relevant samples based on text search in tenant-specific scenarios, where accurate searching can be executed for content associated with one or more tenant accounts of an application/service concurrently in milliseconds even in instances where there may be millions of documents to be searched. As an example, exemplary data shards may be generated and managed for efficient and scalable content retrieval processing including training of a classifier (e.g.

Abstract: The present disclosure relates to processing operations configured to uniquely utilize indexing of content to improve content retrieval processing, particularly when working with large data sets. The techniques described herein enables efficient content retrieval when working with large data sets such as those that may be associated with a plurality of tenants of a data storage application/service. Among other technical advantages, the present disclosure is applicable to train a classifier using relevant samples based on text search in tenant-specific scenarios, where accurate searching can be executed for content associated with one or more tenant accounts of an application/service concurrently in milliseconds even in instances where there may be millions of documents to be searched. As an example, exemplary data shards may be generated and managed for efficient and scalable content retrieval processing including training of a classifier (e.g.

Abstract: The present disclosure relates to processing operations configured to uniquely utilize indexing of content to improve content retrieval processing, particularly when working with large data sets. The techniques described herein enables efficient content retrieval when working with large data sets such as those that may be associated with a plurality of tenants of a data storage application/service. Among other technical advantages, the present disclosure is applicable to train a classifier using relevant samples based on text search in tenant-specific scenarios, where accurate searching can be executed for content associated with one or more tenant accounts of an application/service concurrently in milliseconds even in instances where there may be millions of documents to be searched. As an example, exemplary data shards may be generated and managed for efficient and scalable content retrieval processing including training of a classifier (e.g.

Abstract: One or more techniques and/or systems are provided for interactively associating a semantic concept with a unique term that is input by a user. As the user is creating a document and/or once the user has completed a draft of the document, the document is parsed to identify unique terms (e.g., persons, places, things, services, etc.) in the document. When a unique term is identified, a query is generated to locate one or more semantic concepts (e.g., URLs, URNs, or other identifiers, for example) that are associated with the identified unique term and a notification indicative of the results is generated. From this notification, the user can select whether to associate the unique term with any and/or all of the located semantic concepts. In this way, supplemental content may be added to a document that the user is creating, for example.

Abstract: One or more techniques and/or systems are provided for interactively associating a semantic concept with a unique term that is input by a user. As the user is creating a document and/or once the user has completed a draft of the document, the document is parsed to identify unique terms (e.g., persons, places, things, services, etc.) in the document. When a unique term is identified, a query is generated to locate one or more semantic concepts (e.g., URLs, URNs, or other identifiers, for example) that are associated with the identified unique term and a notification indicative of the results is generated. From this notification, the user can select whether to associate the unique term with any and/or all of the located semantic concepts. In this way, supplemental content may be added to a document that the user is creating, for example.

Abstract: One or more techniques and/or systems are provided for interactively associating a semantic concept with a unique term that is input by a user. As the user is creating a document and/or once the user has completed a draft of the document, the document is parsed to identify unique terms (e.g., persons, places, things, services, etc.) in the document. When a unique term is identified, a query is generated to locate one or more semantic concepts (e.g., URLs, URNs, or other identifiers, for example) that are associated with the identified unique term and a notification indicative of the results is generated. From this notification, the user can select whether to associate the unique term with any and/or all of the located semantic concepts. In this way, supplemental content may be added to a document that the user is creating, for example.

Abstract: One or more techniques and/or systems are provided for interactively associating a semantic concept with a unique term that is input by a user. As the user is creating a document and/or once the user has completed a draft of the document, the document is parsed to identify unique terms (e.g., persons, places, things, services, etc.) in the document. When a unique term is identified, a query is generated to locate one or more semantic concepts (e.g., URLs, URNs, or other identifiers, for example) that are associated with the identified unique term and a notification indicative of the results is generated. From this notification, the user can select whether to associate the unique term with any and/or all of the located semantic concepts. In this way, supplemental content may be added to a document that the user is creating, for example.