Abstract: A method for extracting of data contained in a fixed format electronic document is disclosed. The method is particularly applicable to extracting data from tables in electronic documents and includes reading, by a computer system, the electronic document as a computer image file; segmenting, by the computer system, the computer image file into document sections representative of distinct portions of data; applying a label to each distinct document section; and executing, by the computer system, an optical character recognition algorithm to convert the image file into computer-readable text, wherein segments of the converted text is associated with a respective label indicative of each distinct document section.

Abstract: A long-term memory network method and system for text comprehension. A recurrent neural network can be provided, which includes an external memory module and a long-short term memory unit, wherein said recurrent neural network encodes raw text information into vector representations, forms memories, finds relevant sentences to answer questions, and generates multi-word answers to said questions utilizing the long short term memory unit.

Abstract: A method for extracting of data contained in a fixed format electronic document is disclosed. The method is particularly applicable to extracting data from tables in electronic documents and includes reading, by a computer system, the electronic document as a computer image file; segmenting, by the computer system, the computer image file into document sections representative of distinct portions of data; applying a label to each distinct document section; and executing, by the computer system, an optical character recognition algorithm to convert the image file into computer-readable text, wherein segments of the converted text is associated with a respective label indicative of each distinct document section.

Abstract: A system for answering multiple choice questions includes at least one processor configured to create a question answering model using a training data set. The system is configured to create a balanced data from the imbalanced training data set. The balancing of the imbalanced training data set is achieved by generating synthetic instances of at least one minority category, among a plurality of categories into which the training data set is categorized.

Abstract: A long-term memory network method and system for text comprehension. A recurrent neural network can be provided, which includes an external memory module and a long-short term memory unit, wherein said recurrent neural network encodes raw text information into vector representations, forms memories, finds relevant sentences to answer questions, and generates multi-word answers to said questions utilizing the long short term memory unit.