Abstract: Disclosed are a system, method and apparatus for classification of data in a machine learning system. In one aspect, a method for classification of data in a machine learning system through one or more computer processors is disclosed. Further, generating, through one or more computer processors, a data classifier using a first dataset and determining an accuracy value of the data classifier to achieve a predefined model accuracy threshold. Still further, iterating, through one or more computer processors, calibration of the first dataset based on a set of parameters until the accuracy value matches or exceeds the predefined model accuracy threshold value. Further, the calibration comprises a user input to indicate a correctness of a presented subset of data from a second dataset and using the above to generate an enhanced data classifier for the classification of data.

Abstract: Techniques for agent-assist systems to provide context-aware, subdocument-granularity recommended answers to agents that are attempting to answer queries of users. The agent-assist system may obtain collections of documents that include information for responding to queries, and analyze those documents to identify subdocuments that are associated with different semantics or meanings. Subsequently, any queries received can be analyzed to identify their semantics, and relevant subdocuments can be identified as having similar semantics. When the agent-assist system presents the agent with the relevant documents, it may highlight or otherwise indicate the relevant subdocument within the document for quick identification by the agent. Further, the agent-assist system may collect feedback from the agent and/or user to determine a relevancy of the recommended answers. The agent-assist system can use the feedback to improve the quality of the recommended answers provided to the agents.

Abstract: Techniques for agent-assist systems to provide context-aware, subdocument-granularity recommended answers to agents that are attempting to answer queries of users. The agent-assist system may obtain collections of documents that include information for responding to queries, and analyze those documents to identify subdocuments that are associated with different semantics or meanings. Subsequently, any queries received can be analyzed to identify their semantics, and relevant subdocuments can be identified as having similar semantics. When the agent-assist system presents the agent with the relevant documents, it may highlight or otherwise indicate the relevant subdocument within the document for quick identification by the agent. Further, the agent-assist system may collect feedback from the agent and/or user to determine a relevancy of the recommended answers. The agent-assist system can use the feedback to improve the quality of the recommended answers provided to the agents.

Abstract: Disclosed are a system, method and apparatus for classification of data in a machine learning system. In one aspect, a method for classification of data in a machine learning system through one or more computer processors is disclosed. Further, generating, through one or more computer processors, a data classifier using a first dataset and determining an accuracy value of the data classifier to achieve a predefined model accuracy threshold. Still further, iterating, through one or more computer processors, calibration of the first dataset based on a set of parameters until the accuracy value matches or exceeds the predefined model accuracy threshold value. Further, the calibration comprises a user input to indicate a correctness of a presented subset of data from a second dataset and using the above to generate an enhanced data classifier for the classification of data.