Text Iq, Inc.

Relativity Oda LLC

Jasneet Singh Sabharwal

Apoorv Agarwal

Ethan Benjamin

Ayushi Dalmia

Somya Anand

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.

System and method for automatic identification of legal entities

Identifying documents that contain potential code words using a machine learning model. In some embodiments, a method may include receiving documents, identifying a first corpus and a second corpus in the documents, extracting a first set of word embeddings from the first corpus and a second set of word embeddings from the second corpus, generating a first vector space for the first set of word embeddings and a second vector space for the second set of word embeddings using a machine learning model, performing a vector rotation to improve alignment of the first set of word embeddings with the second set of word embeddings, identifying a word embedding in the first vector space that is not aligned with a corresponding word embedding in the second vector space as a potential code word, and identifying one or more documents that contain the potential code word in the first corpus.

Identifying documents that contain potential code words using a machine learning model

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.

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Vancouver, Canada

Jasneet Singh Sabharwal