Sp Global, Inc.

Delphine Vendryes

Baojia Tong

Chester Curme

Hamima Halim

Maxim Sokolov

Vadym Barda

Andrey Ryazanov

David Relyea

Nabeel El-Husayni

Jilin Wang

Matthew Theisen

Training and using a machine learning model for data extraction is provided. The method comprises receiving keys of interest received from a user through an interface and receiving a batch of documents containing unstructured text. Unstructured text of a first document is processed to extract structured text. The model predicts text classifications of the structured text according to the keys of interest. The predicted text classifications are output to the user through the interface. Annotations to correct any incorrect predictions are received from the user, and the model is retrained according to the annotations. The above steps are repeated for less than ten additional documents from the batch until the model has been trained to predict text classifications with a specified level of accuracy. The trained model then classifies extracted structured text in the remaining documents in the batch.

Information extraction for unstructured text documents

A method of extracting data from documents is provided. The method comprises receiving input of a number of documents and input of a schema of data items available for extraction from the documents. The documents are parsed into a machine-readable representation, and data items in the machine-readable representation are identified according to the schema. Interpretations of data items are propagated within the documents to disambiguate identified data items, and identified data items are matched with other data items in the documents according to the schema. Only identified data items that include a minimal set of interpretations specified by the schema are extracted.

Schema-informed extraction for unstructured data

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Venice, CA, United States of America

Delphine Vendryes