100.Co, LLC

Sansa Ai Inc.

Daash Intelligence, Inc.

Stefan Krawczyk

Robert J Munro

Schuyler D Erle

Gary C King

Jason Brenier

Brendan D Callahan

Jessica D Long

Tripti Saxena

Rob Voigt

Tyler J Schnoebelen

Paul A Tepper

Veena Basavaraj

Systems, methods, and apparatuses are presented for a novel natural language tokenizer and tagger. In some embodiments, a method for tokenizing text for natural language processing comprises: generating from a pool of documents, a set of statistical models comprising one or more entries each indicating a likelihood of appearance of a character/letter sequence in the pool of documents; receiving a set of rules comprising rules that identify character/letter sequences as valid tokens; transforming one or more entries in the statistical models into new rules that are added to the set of rules when the entries indicate a high likelihood; receiving a document to be processed; dividing the document to be processed into tokens based on the set of statistical models and the set of rules, wherein the statistical models are applied where the rules fail to unambiguously tokenize the document; and outputting the divided tokens for natural language processing.

Intelligent system that dynamically improves its knowledge and code-base for natural language understanding

Methods, apparatuses and computer readable medium are presented for generating a natural language model. A method for generating a natural language model comprises: selecting from a pool of documents, a first set of documents to be annotated; receiving annotations of the first set of documents elicited by first human readable prompts; training a natural language model using the annotated first set of documents; determining documents in the pool having uncertain natural language processing results according to the trained natural language model and/or the received annotations; selecting from the pool of documents, a second set of documents to be annotated comprising documents having uncertain natural language processing results; receiving annotations of the second set of documents elicited by second human readable prompts; and retraining a natural language model using the annotated second set of documents.

Optimization techniques for artificial intelligence

Growing community of inventors

Menlo Park, CA, United States of America

Stefan Krawczyk