Sparkcognition, Inc.

Erik Skiles

Syed Mohammad Ali

Keith D Moore

Joshua Bronson

Jaidev Amrite

William McNeill

Jashmi Lagisetty

Devan Plantamura

A method includes obtaining a query in a base language and translating the query to generate one or more translated queries each in a respective target language. The method also includes searching one or more sets of electronic files based on the one or more translated queries to generate target-language search results, where each translated query is used to search one or more electronic files that include content in the respective target language of the translated query. The method also includes, based on the target-language search results, scheduling one or more electronic files of the one or more sets of electronic files for at least partial translation to the base language.

Natural-language processing across multiple languages

A method of generating a searchable representation of an electronic document includes obtaining an electronic document specifying a graphical layout of content items including text. The method also includes determining pixel data representing the graphical layout of the content items and providing input data based, at least in part, on the pixel data to a document parsing model. The document parsing model is trained to detect functional regions within the graphical layout based on the input data, assign boundaries to the functional regions based on the input data, and assign a category label to each functional region that is detected. The method also includes matching portions of the text to corresponding functional regions based on the boundaries assigned to the functional regions and locations associated with the portions of the text and storing data representing the content items, the functional regions, and the category labels in a searchable data structure.

Searchable data structure for electronic documents

A particular method includes automatically generating, at a processor of a computing device, annotation data indicating that a column of a data table corresponds to a particular class of an ontology. The method also includes storing the annotation data. The method further includes receiving a natural language query. The method also includes generating a second query based on detecting a match between at least one term of the natural language query and the annotation data. The method further includes determining a response to the second query. The method also includes outputting the response to the second query as a response to the natural language query.

Natural language querying of data in a structured context

A method includes performing, by a computing device, a clustering operation to group documents of a document corpus into clusters in a feature vector space. The document corpus includes one or more labeled documents and one or more unlabeled documents. Each of the one or more labeled documents is assigned to a corresponding class in classification data associated with the document corpus, and each of the one or more unlabeled document is not assigned to any class in the classification data. The method also includes generating, by the computing device, a prompt requesting classification of a particular document of the document corpus, where the particular document is selected based on a distance between the particular document and a labeled document of the one or more labeled documents.

Generation of document classifiers

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.

Generation of text classifier training data

A method includes receiving, via a graphical user interface including a plurality of document elements and a plurality of class elements, user input associating a first document element of the plurality of document elements with a first class element of the plurality of class elements. Each document element represents a corresponding document of a plurality of documents, and each class element represents a corresponding class of a plurality of classes. The method also includes generating a document classifier using supervised training data, where the supervised training data indicates, based on the user input, that a first document represented by the first document element is assigned to a first class associated with the first class element.

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Manor, TX, United States of America

Erik Skiles