Abstract: A document management system trains a machine-learned model using a first training set of tagged documents to, when applied to a document, tag one or more portions of the document. The document management system applies the machine-learned model to a target document. One or more portions of the target document incorrectly tagged by the machine-learned model are identified. A feature vector representative of the target document is generated. Each entry of the feature vector is representative of a characteristic of the target document without including private information from the target document. The document management system queries a corpus of documents using the feature vector to identify a set of documents that correspond to the feature vector. A second training set of tagged documents is generated using the identified set of documents. The document management system retrains the machine-learned model using the second training set of tagged documents.

Abstract: A user is writing an email or other message. The message is inspected for a predetermined word and for at least one of: a predetermined phrase or a template match. A score is generated if the predetermined phrase or the template match is detected. A suggestion is provided to a user that the message may need an attachment if the predetermined word is detected or if the score is greater than a predetermined score. The inspection may begin when the user begins creating the message, indicates that the message should be sent, or at some point in between. The user may provide feedback that the suggestion is accepted or that the suggestion is rejected.

Abstract: A user is writing an email or other message. The message is inspected for a predetermined word and for at least one of: a predetermined phrase or a template match. A score is generated if the predetermined phrase or the template match is detected. A suggestion is provided to a user that the message may need an attachment if the predetermined word is detected or if the score is greater than a predetermined score. The inspection may begin when the user begins creating the message, indicates that the message should be sent, or at some point in between. The user may provide feedback that the suggestion is accepted or that the suggestion is rejected.

Abstract: A user is writing an email or other message. The message is inspected for a predetermined word and for at least one of: a predetermined phrase or a template match. A score is generated if the predetermined phrase or the template match is detected. A suggestion is provided to a user that the message may need an attachment if the predetermined word is detected or if the score is greater than a predetermined score. The inspection may begin when the user begins creating the message, indicates that the message should be sent, or at some point in between. The user may provide feedback that the suggestion is accepted or that the suggestion is rejected.

Abstract: Automatically suggesting content, for example, documents, contacts information, tasks, calendar items, and the like, for inclusion into an electronic collaborative workspace is provided. Given a short description of a given project or collaborative workspace, an initial project membership list, and identification of local or external content repositories, content associated with the project or workspace may be retrieved and recommended to one or more users for inclusion into the project or collaborative workspace. Thus, the time and effort required for initially populating an electronic workspace with needed content is dramatically reduced.

Abstract: A user is writing an email or other message. The message is inspected for a predetermined word and for at least one of: a predetermined phrase or a template match. A score is generated if the predetermined phrase or the template match is detected. A suggestion is provided to a user that the message may need an attachment if the predetermined word is detected or if the score is greater than a predetermined score. The inspection may begin when the user begins creating the message, indicates that the message should be sent, or at some point in between. The user may provide feedback that the suggestion is accepted or that the suggestion is rejected.

Abstract: A user is writing an email or other message. A text processing layer prepares the message for analysis and performs some initial analysis and preparation such as a sentence separator, a “tokenizer”, a “unique body” identification, and/or possibly other filtering or analysis routines. An analytics layer analyzes the message using a “regular expressions” analysis, a statistical natural language processing analysis, and/or a template matching analysis. If an attachment is indicated, a suggestion to add an attachment is provided to the user. The analysis may begin when the user begins creating the message, indicates that the message should be sent, or at some point in between. The user may provide feedback that the suggestion is accepted or that the suggestion is rejected.

Abstract: A user is writing an email or other message. A text processing layer prepares the message for analysis and performs some initial analysis and preparation such as a sentence separator, a “tokenizer”, a “unique body” identification, and/or possibly other filtering or analysis routines. An analytics layer analyzes the message using a “regular expressions” analysis, a statistical natural language processing analysis, and/or a template matching analysis. If an attachment is indicated, a suggestion to add an attachment is provided to the user. The analysis may begin when the user begins creating the message, indicates that the message should be sent, or at some point in between. The user may provide feedback that the suggestion is accepted or that the suggestion is rejected.

Abstract: Automatically suggesting content, for example, documents, contacts information, tasks, calendar items, and the like, for inclusion into an electronic collaborative workspace is provided. Given a short description of a given project or collaborative workspace, an initial project membership list, and identification of local or external content repositories, content associated with the project or workspace may be retrieved and recommended to one or more users for inclusion into the project or collaborative workspace. Thus, the time and effort required for initially populating an electronic workspace with needed content is dramatically reduced.

Abstract: An automatic discovery of content to add to a data store for a project is disclosed. A data item may be parsed for data features that are contextually relevant to a given project or task. Discovered interesting data may be extracted and mapped to various search mechanisms. A search may be built and applied to various data sources to discover data items based on the contextually relevant data features. Search results from various search mechanisms may be displayed in a single user interface and may be presented to a user.

Abstract: An analysis module, when triggered by a synchronization framework when a new data item is added to a project data store, runs a series of analysis feature extractors on the new content. An analysis may be conducted, and features of interest may be extracted from the data item. The analysis utilizes natural language processing, as well as other technologies, to provide an automatic or semi-automatic extraction of information. The extracted features of interest are saved as metadata within the project data store, and are associated with the data item from which it was extracted. The analysis module may be utilized to discover additional information that may be gleaned from content that is already in the project data store.

Abstract: A space in which a user may organize data and collaborate with other users is provided. Project data and metadata may be saved into a reusable template and stored in a repository, wherein other users may discover and download the template for use as a resource for a related project type. A template may contain various document types that may expose a breadth of applications and application functionalities that may be utilized for a given task or project. Questions and answers, task templates, and terminology may also be provided. Project templates may allow for collaboration on projects, wherein business and/or social groups may collaborate quickly, easily, and more completely.

Abstract: Project-related data may be aggregated from various data sources, given context, and may be stored in a data repository or organizational knowledge base that may be available to and accessed by others. Documents, emails, contact information, calendar data, social networking data, and any other content that is related to a project may be brought together within a single user interface, irrespective of its data type. A user may organize and understand content, discover relevant information, and act on it without regard to where the information resides or how it was created.