Pryon Incorporated

Chul Sung

David Nahamoo

Vaibhava Goel

Etienne Marcheret

Steven John Rennie

Marie Wenzel Meteer

Ellen Eide Kislal

Igor Roditis Jablokov

Soonthorn Ativanichayaphong

Carmi Rothberg

Ajinkya Jitendra Zadbuke

John Pruitt

Dominique O'Donnell

Dominique O'donnell

A question-answering system that receive a natural-language question includes a database to provide a basis for that answer and a structured-query generator that constructs a structured query from the question and uses it to obtain an answer to the question from the database. The method further comprises generating a first answer to said first question, wherein generating said first answer comprises causing said question to traverse a first path through said question-answering system, wherein the first path comprises a knowledge base constructed from content of the document collection using a machine learning BERT model; and causing said question to traverse a second path through said question-answering system to generate a second answer to said question, said second answer differing from said first answer, wherein the second path comprises a structured query generator and the database, the database being distinct from the knowledge base.

Question-answering system for answering relational questions by utilizing two paths where at least one path uses BERT model

Disclosed are methods, systems, devices, apparatus, media, and other implementations that include a method for document processing (particularly for training of a machine learning question answering platform, and for ingestion of documents). The method includes obtaining a question dataset (e.g., either from public or private repositories of questions) comprising one or more source questions for document processing by a machine learning question-and-answer system that provides answer data in response to question data submitted by a user, modifying a source question from the question dataset to generate one or more augmented questions with equivalent semantic meanings as that of the source question, and processing a document with the one or more augmented questions.

Methods and systems for improved document processing and information retrieval

A question-answering system that receive a natural-language question includes a database to provide a basis for that answer and a structured-query generator that constructs a structured query from the question and uses it to obtain an answer to the question from the database.

Visual structure of documents in question answering

Growing community of inventors

Fort Lee, NJ, United States of America

Chul Sung