Text Iq, Inc.

International Business Machines Corporation

Apoorv Agarwal

Ethan Benjamin

Jasneet Singh Sabharwal

Aditya Anand Kalyanpur

Jennifer Chu-Carroll

James W Murdock, Iv

Adam P Lally

Lorrie Ann Tomek

Wlodek Wlodzimierz Zadrozny

Kenneth James Barker

Christopher A Welty

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.

Automatic document classification

A method for identifying and ranking potentially privileged documents using a machine learning topic model may include receiving a set of documents. The method may also include, for each of two or more documents in the set of documents, extracting a set of spans from the document, generating, using a machine learning topic model, a set of topics and a subset of legal topics for the set of spans, generating a vector of probabilities for each span with a probability being assigned to each topic in the set of topics for the span, assigning a score to one or more spans in the set of spans by summing the probabilities in the vector that are assigned to a topic in the subset of legal topics, and assigning a score to the document. The method may further include ranking the two or more documents by their assigned scores.

Identifying and ranking potentially privileged documents using a machine learning topic model

According to an aspect, a heterogeneous graph in a data store is accessed. The heterogeneous graph includes a plurality of nodes having a plurality of node types. The nodes are connected by edges having a plurality of relation types. One or more intermediary graphs are created based on the heterogeneous graph. The intermediary graphs include intermediary nodes that are the relation types of the edges of the heterogeneous graph and include intermediary links between the intermediary nodes based on shared instances of the nodes between relation types in the heterogeneous graph. The intermediary graphs are traversed to find sets of relations based on intermediary links according to a template. An inference rule is extracted from the heterogeneous graph based on finding sets of relations in the intermediary graphs. The inference rule defines an inferred relation type between at least two of the nodes of the heterogeneous graph.

Extraction of inference rules from heterogeneous graphs

Methods/systems receive a question and automatically search sources of data containing passages to produce candidate answers to the question. The searching identifies passages that support each of the candidate answers based on scoring features that indicate whether the candidate answers are correct answers to the question. These methods/systems automatically create a scoring feature-specific matrix for each scoring feature. Each scoring feature-specific matrix has a score field for each different combination of text passage and question term (vector), and each score field holds a score value (vector value) indicating how each different combination of text passage and question term supports the candidate answers as being a correct answer to the question. Next, such methods/systems automatically combine multiple such vectors to produce a combined vector score for each of the candidate answers, and then rank the candidate answers based on the combined scores.

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Apoorv Agarwal