Abstract: A method includes receiving a set of documents associated with data discovery. The method further includes receiving, for each document in a subset of the set of documents, an indication of relevancy or non-relevancy of the document for an issue. The method further includes modifying one or more parameters for a machine-learning model based on the indication of relevancy or non-relevancy. The method further includes outputting, for each document in the set of documents, by a machine-learning model, a prediction probability of relevancy to an issue associated with the data discovery, and a ranking of the set of documents based on the prediction probability of relevancy. The method further includes generating a user interface that includes a sampling of the documents for review by a user, where each document is associated with a predicted relevancy tag or a predicted non-relevancy tag.

Abstract: A method includes receiving a set of documents associated with data discovery. The method further includes receiving, for each document in a subset of the set of documents, an indication of relevancy or non-relevancy of the document for an issue. The method further includes modifying one or more parameters for a machine-learning model based on the indication of relevancy or non-relevancy. The method further includes outputting, for each document in the set of documents, by a machine-learning model, a prediction probability of relevancy to an issue associated with the data discovery, and a ranking of the set of documents based on the prediction probability of relevancy. The method further includes generating a user interface that includes a sampling of the documents for review by a user, where each document is associated with a predicted relevancy tag or a predicted non-relevancy tag.

Abstract: A method includes receiving a set of documents associated with data discovery. The method further includes receiving, for each document in a subset of the set of documents, an indication of relevancy or non-relevancy of the document for an issue. The method further includes modifying one or more parameters for a machine-learning model based on the indication of relevancy or non-relevancy. The method further includes outputting, for each document in the set of documents, by a machine-learning model, a prediction probability of relevancy to an issue associated with the data discovery, and a ranking of the set of documents based on the prediction probability of relevancy. The method further includes generating a user interface that includes a sampling of the documents for review by a user, where each document is associated with a predicted relevancy tag or a predicted non-relevancy tag.

Abstract: A method includes receiving a set of documents related to data discovery issues, wherein at least a first data discovery issue is unrelated to a second data discovery issue. The method further includes generating a map of terms and words for the set of documents that correspond to concepts. The method further includes providing a user interface that includes a search analytics tool to a user associated with the first data discovery issue.

Abstract: A method includes receiving a first set of documents. The method further includes generating a map of terms and words based on the first set of documents, where the map of terms and words correspond to concepts. The method further includes receiving an initial document relating to a data discovery issue. The method further includes identifying at least one of a key term, a person, a date, and a key document based on the map of terms and words. The method further includes generating a story that includes the at least one of the key term, the person, the date, and the key document.

Abstract: A method includes receiving a first set of documents that correspond to data discovery documents. The method further includes generating a map of terms and words based on the first set of documents, the map of terms and words corresponding to concepts. The method further includes receiving from a first user an initial document relating to a data discovery issue. The method further includes requesting the user to provide an identification of at least one of an initial name of a first person, an initial date, and an initial keyword related to the legal issue. The method further includes generating at least one of a similar name of a second person, a similar date, and a similar keyword based on the map of terms and words. The method further includes identifying a review document based on the review document including at least one of the similar name of the second person, the similar date, and the similar keyword.

Abstract: A method includes receiving a first set of documents that correspond to data discovery documents. The method further includes generating a map of terms and words based on the first set of documents, the map of terms and words corresponding to concepts. The method further includes receiving from a first user an initial document relating to a data discovery issue. The method further includes requesting the user to provide an identification of at least one of an initial name of a first person, an initial date, and an initial keyword related to the legal issue. The method further includes generating at least one of a similar name of a second person, a similar date, and a similar keyword based on the map of terms and words. The method further includes identifying a review document based on the review document including at least one of the similar name of the second person, the similar date, and the similar keyword.

Abstract: A method includes receiving a set of documents related to data discovery issues, wherein at least a first data discovery issue is unrelated to a second data discovery issue. The method further includes generating a map of terms and words for the set of documents that correspond to concepts. The method further includes providing a user interface that includes a search analytics tool to a user associated with the first data discovery issue.

Abstract: Implementations generally relate to providing document analytics. In some implementations, a method includes receiving a plurality of documents related to e-discovery. The method further includes generating a graphical user interface that includes an analytics panel that provides analytics information about the plurality of documents. The method further includes receiving, from one or more users, manual tags for one or more documents of the plurality of documents. The method further includes performing a first iteration that determines a first group of documents that are improperly associated with one or more of the manual tags based on at least one of content and metadata of the plurality of documents. The method further includes performing a second iteration that determines a second group of documents that are improperly associated with one or more of the manual tags based on the reclassification.

Abstract: A method includes receiving a set of documents related to data discovery issues, wherein at least a first data discovery issue is unrelated to a second data discovery issue. The method further includes generating a map of terms and words for the set of documents that correspond to concepts. The method further includes providing a user interface that includes a search analytics tool to a user associated with the first data discovery issue.

Abstract: Implementations generally relate to processing similar documents. In some implementations, a method includes receiving a plurality of documents related to e-discovery. The method further includes determining a seed document from the plurality of documents. The method further includes receiving a search request to search at least one selection of text in the seed document. The method further includes identifying other documents from the plurality of documents based on a similarity between text in the other documents and the at least one selection of text in the seed document. The method further includes generating a graphical user interface that includes a similarity panel that provides similarity data between text in the other documents and the at least one selection of text in the seed document.

Abstract: Implementations generally relate to processing similar documents. In some implementations, a method includes receiving a plurality of documents related to e-discovery. The method further includes determining a seed document from the plurality of documents. The method further includes receiving a search request to search at least one selection of text in the seed document. The method further includes identifying other documents from the plurality of documents based on a similarity between text in the other documents and the at least one selection of text in the seed document. The method further includes generating a graphical user interface that includes a similarity panel that provides similarity data between text in the other documents and the at least one selection of text in the seed document.