Sparkcognition, Inc.

William McNeill

Erik Skiles

Jaidev Amrite

A method includes obtaining, at a device, a hierarchical structure representing a graphical layout of content items of an electronic document, the content items including at least text. The method also includes generating a word embedding representing a word of the electronic document. The method further includes determining position information of a location of the word in the electronic document. The method also includes determining a descriptor that indicates a relationship of the location to the hierarchical structure. The method further includes providing input data to a machine learning model to generate a semantic region category label of a semantic region of the electronic document. The semantic region includes the word. The input data includes the word embedding, the position information, and the descriptor.

Searchable data structure for electronic documents

A method includes receiving input designating a term of interest in a document of a document corpus and determining a target context embedding representing a target word group that includes the term of interest and context words located in the document proximate to the term of interest. The method also includes identifying, from among the document corpus, a first candidate word group that is semantically similar to the target word group and a second candidate word group that is semantically dissimilar to the target word group. The method further includes receiving user input identifying at least a portion of the first candidate word group as associated with a first label and identifying at least a portion of the second candidate word group as not associated with the first label. The method also includes generating labeled training data based on the user input to train a text classifier.

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Austin, TX, United States of America

William McNeill