Abstract: A method to automatically classify emails may include generating multiple entity data objects using entities identified in receiver and sender fields of emails and categorizing the multiple entity data objects into a first set of data objects and a second set of data objects. The method may also include extracting all tokens from each email and searching the extracted tokens for tokens associated with the data objects of the first set of data objects. The method may further include identifying the emails that include the extracted tokens that are associated with the data objects of the first set of data objects, identifying a particular data object of the first set of data objects to which an identified email corresponds, and automatically classifying the identified email in the first category in response to identifying the particular data object of the first set of data objects to which an identified email corresponds.

Abstract: Systems, methods, and computer readable media for self-training a parser of electronic communication documents, such as emails, are provided. These techniques may include applying a parser to a batch of electronic communication documents to identify entities included in unstructured text. The outputs of the parser are used to identify entries in a metadata file associated with the electronic communication documents to generate training data for the parser. The parser is then re-trained using the training data and applied to an additional batch of documents. Through this process, the systems, methods, and computer readable media are able to re-train the parser without obtaining manual annotations of electronic communication documents.

Abstract: Systems, methods, and computer readable media for identifying entities as legal entities are provided. These techniques may include accessing a corpus of documents and applying a persona prediction machine learning algorithm to classify entities associated with the corpus of documents. The persona prediction machine learning algorithm may include two layers. A first layer includes applying a signature block classifier that analyzes signature blocks of the entities. A second layer includes applying an entity classifier that analyzes a plurality of documents and/or network graphs associated with the entities. An entity database is updated to indicate the output of the persona prediction machine learning algorithm based on the signature block classifier and/or the entity classifier.

Abstract: Systems, methods, and computer readable media for intelligent generation of a privilege log are provided. These techniques may include accessing a corpus of documents and applying an unsupervised machine learning model thereto to identify a plurality of topics. Using the identified topics, the techniques include associating a plurality of categories with the identified topics and executing a classifier training model to train classifiers corresponding to the categories. These classifiers are then applied to the corpus of documents to label the documents. Subsequently, the techniques automatically generate a privilege log based upon the labels applied to the documents by the classifiers.

Abstract: A method to automatically classify emails may include generating multiple entity data objects using entities identified in receiver and sender fields of emails and categorizing the multiple entity data objects into a first set of data objects and a second set of data objects. The method may also include extracting all tokens from each email and searching the extracted tokens for tokens associated with the data objects of the first set of data objects. The method may further include identifying the emails that include the extracted tokens that are associated with the data objects of the first set of data objects, identifying a particular data object of the first set of data objects to which an identified email corresponds, and automatically classifying the identified email in the first category in response to identifying the particular data object of the first set of data objects to which an identified email corresponds.

Abstract: A method to automatically classify emails may include generating multiple entity data objects using entities identified in receiver and sender fields of emails and categorizing the multiple entity data objects into a first set of data objects and a second set of data objects. The method may also include extracting all tokens from each email and searching the extracted tokens for tokens associated with the data objects of the first set of data objects. The method may further include identifying the emails that include the extracted tokens that are associated with the data objects of the first set of data objects, identifying a particular data object of the first set of data objects to which an identified email corresponds, and automatically classifying the identified email in the first category in response to identifying the particular data object of the first set of data objects to which an identified email corresponds.

Abstract: A method to automatically classify emails may include generating multiple entity data objects using entities identified in receiver and sender fields of emails and categorizing the multiple entity data objects into a first set of data objects and a second set of data objects. The method may also include extracting all tokens from each email and searching the extracted tokens for tokens associated with the data objects of the first set of data objects. The method may further include identifying the emails that include the extracted tokens that are associated with the data objects of the first set of data objects, identifying a particular data object of the first set of data objects to which an identified email corresponds, and automatically classifying the identified email in the first category in response to identifying the particular data object of the first set of data objects to which an identified email corresponds.

Abstract: A method to automatically classify emails may include generating multiple entity data objects using entities identified in receiver and sender fields of emails and categorizing the multiple entity data objects into a first set of data objects and a second set of data objects. The method may also include extracting all tokens from each email and searching the extracted tokens for tokens associated with the data objects of the first set of data objects. The method may further include identifying the emails that include the extracted tokens that are associated with the data objects of the first set of data objects, identifying a particular data object of the first set of data objects to which an identified email corresponds, and automatically classifying the identified email in the first category in response to identifying the particular data object of the first set of data objects to which an identified email corresponds.

Abstract: A method may include obtaining a set of documents. Text objects from a text object database generated based on the set of documents may be compared to a search term object to identify relevant text objects that match the search term object. A context object for each of the relevant text objects that indicate usage of the relevant text objects within the documents corresponding to the relevant text objects may be determined, and context objects may be grouped according to similarities between the context objects. A first or second classification may be applied to each context object based on one or more criteria, and the first and second classifications may also be applied to each group based on the classifications of the context objects within the groups. Documents within the set of documents may be given the first or second classifications based on relations and similarities to the classified groups.