Abstract: The technology implements diversity sampling for a technology-assisted review of documents. An apparatus obtains an unlabeled set of documents and constructs a first batch of documents. The apparatus obtains labels for the documents and constructs a classification model using the labeled documents. The apparatus logs a found rate of a subsequent batch of documents from the unlabeled set of documents, the subsequent batch of documents being selected based on a comparison to the classification model. The apparatus determines that the classification model requires further training based on the found rate of the subsequent batch of documents and constructs a second batch of documents that includes an amount of diversity, which may be based on the found rate. The apparatus obtains labels for the second batch of documents and updates the model using the labeled second batch of documents. The method may be repeated to continue to refine the classification model.

Abstract: Systems and methods enable convenient and accurate searching, filtering, reviewing, and classification of electronic documents without the loss of metadata. A communication data source file is parsed into conversation-specific files that include message content and metadata. The message content and metadata are displayed on a computing device operated by a reviewer. To streamline the review process, the reviewer can filter display of the message content according to various metadata categories as well as search conversation-specific files using the metadata categories.