Abstract: Embodiments provide for intelligent auto filing of documents to enterprise content management workspaces. One embodiment of a method includes receiving a document to auto file to a workspace of a content management system; detecting an indicator of an entity from the text of a document, the indicator of the entity corresponding to a value of a workspace attribute; determining a result set of candidate records based on querying a set of workspace data for workspaces with the workspace attribute value corresponding to the indicator; detecting mentions in the document text that match attribute values from the candidate records; generating a score for each candidate record based on the mentions detected in the text that match the attribute values from the candidate record; linking the document to an entity based on the scores for the candidate record; and automatically storing the document to a workspace based on the linking.

Abstract: Embodiments provide for intelligent auto filing of documents to enterprise content management workspaces. One embodiment of a method includes receiving a document to auto file to a workspace of a content management system; detecting an indicator of an entity from the text of a document, the indicator of the entity corresponding to a value of a workspace attribute; determining a result set of candidate records based on querying a set of workspace data for workspaces with the workspace attribute value corresponding to the indicator; detecting mentions in the document text that match attribute values from the candidate records; generating a score for each candidate record based on the mentions detected in the text that match the attribute values from the candidate record; linking the document to an entity based on the scores for the candidate record; and automatically storing the document to a workspace based on the linking.

Abstract: A bipartite application implements a table auto-completion (TAC) algorithm on the client side and the server side. A client module runs a local model of the TAC algorithm on a user device and a server module runs a global model of the TAC algorithm on a server machine. The local model is continuously adapted through on-the-fly training, with as few as one negative example, to perform TAC on the client side, one document at a time. Knowledge thus learned by the local model is used to improve the global model on the server side. The global model can be utilized to automatically and intelligently extract table information from a large number of documents with significantly improved accuracy, requiring minimal human intervention even on complex tables.

Abstract: A bipartite application implements a table auto-completion (TAC) algorithm on the client side and the server side. A client module runs a local model of the TAC algorithm on a user device and a server module runs a global model of the TAC algorithm on a server machine. The local model is continuously adapted through on-the-fly training, with as few as a negative example, to perform TAC on the client side, one document at a time. Knowledge thus learned by the local model is used to improve the global model on the server side. The global model can be utilized to automatically and intelligently extract table information from a large number of documents with significantly improved accuracy, requiring minimal human intervention even on complex tables.

Abstract: Embodiments provide for intelligent auto filing of documents to enterprise content management (ECM) system workspaces. Embodiments may include maintaining a database of ECM information including a plurality of enterprise workspaces having attributes; based on the ECM information, generating a knowledge graph comprising nodes for enterprise workspaces and edges for relationships between enterprise workspaces; receiving a document for filing in one of the enterprise workspaces; detecting a plurality of indicators in the document text and evaluating the indicators to generate a subset of strong indicators in the plurality of indicators; querying the knowledge graph based on the strong indicators to generate a set of candidate enterprise workspaces; comparing the set of candidate enterprise workspace attributes to the strong indicators to determine a score of each candidate enterprise workspace; and based on said scores, linking and storing the document to one of the candidate enterprise workspaces.

Abstract: Embodiments provide for intelligent auto filing of documents to enterprise content management workspaces. One embodiment of a method includes receiving a document to auto file to a workspace of a content management system; detecting an indicator of an entity from the text of a document, the indicator of the entity corresponding to a value of a workspace attribute; determining a result set of candidate records based on querying a set of workspace data for workspaces with the workspace attribute value corresponding to the indicator; detecting mentions in the document text that match attribute values from the candidate records; generating a score for each candidate record based on the mentions detected in the text that match the attribute values from the candidate record; linking the document to an entity based on the scores for the candidate record; and automatically storing the document to a workspace based on the linking.

Abstract: A bipartite application implements a table auto-completion (TAC) algorithm on the client side and the server side. A client module runs a local model of the TAC algorithm on a user device and a server module runs a global model of the TAC algorithm on a server machine. The local model is continuously adapted through on-the-fly training, with as few as a negative example, to perform TAC on the client side, one document at a time. Knowledge thus learned by the local model is used to improve the global model on the server side. The global model can be utilized to automatically and intelligently extract table information from a large number of documents with significantly improved accuracy, requiring minimal human intervention even on complex tables.

Abstract: A bipartite application implements a table auto-completion (TAC) algorithm on the client side and the server side. A client module runs a local model of the TAC algorithm on a user device and a server module runs a global model of the TAC algorithm on a server machine. The local model is continuously adapted through on-the-fly training, with as few as a negative example, to perform TAC on the client side, one document at a time. Knowledge thus learned by the local model is used to improve the global model on the server side. The global model can be utilized to automatically and intelligently extract table information from a large number of documents with significantly improved accuracy, requiring minimal human intervention even on complex tables.

Abstract: A bipartite application implements a table auto-completion (TAC) algorithm on the client side and the server side. A client module runs a local model of the TAC algorithm on a user device and a server module runs a global model of the TAC algorithm on a server machine. The local model is continuously adapted through on-the-fly training, with as few as a negative example, to perform TAC on the client side, one document at a time. Knowledge thus learned by the local model is used to improve the global model on the server side. The global model can be utilized to automatically and intelligently extract table information from a large number of documents with significantly improved accuracy, requiring minimal human intervention even on complex tables.

Abstract: A bipartite application implements a table auto-completion (TAC) algorithm on the client side and the server side. A client module runs a local model of the TAC algorithm on a user device and a server module runs a global model of the TAC algorithm on a server machine. The local model is continuously adapted through on-the-fly training, with as few as a negative example, to perform TAC on the client side, one document at a time. Knowledge thus learned by the local model is used to improve the global model on the server side. The global model can be utilized to automatically and intelligently extract table information from a large number of documents with significantly improved accuracy, requiring minimal human intervention even on complex tables.

Abstract: A bipartite application implements a table auto-completion (TAC) algorithm on the client side and the server side. A client module runs a local model of the TAC algorithm on a user device and a server module runs a global model of the TAC algorithm on a server machine. The local model is continuously adapted through on-the-fly training, with as few as a negative example, to perform TAC on the client side, one document at a time. Knowledge thus learned by the local model is used to improve the global model on the server side. The global model can be utilized to automatically and intelligently extract table information from a large number of documents with significantly improved accuracy, requiring minimal human intervention even on complex tables.