公开(公告)号：US20210026846A1

公开(公告)日：2021-01-28

申请号：US16949076

申请日：2020-10-13

Applicant: GOOGLE LLC

Inventor： Amarnag Subramanya ,  Fernando Pereira ,  Ni Lao ,  John Blitzer ,  Rahul Gupta

IPC: G06F16/245

Abstract: Implementations include systems and methods for querying a data graph. An example method includes receiving a machine learning module trained to produce a model with multiple features for a query, each feature representing a path in a data graph. The method also includes receiving a search query that includes a first search term, mapping the search query to the query, and mapping the first search term to a first entity in the data graph. The method may also include identifying a second entity in the data graph using the first entity and at least one of the multiple weighted features, and providing information relating to the second entity in a response to the search query. Some implementations may also include training the machine learning module by, for example, generating positive and negative training examples from an answer to a query.

公开(公告)号：US11947917B2

公开(公告)日：2024-04-02

申请号：US17672364

申请日：2022-02-15

Applicant: Google LLC

Inventor： Ni Lao ,  Jiazhong Nie ,  Fan Yang

IPC: G06F40/30 ,  G06F16/9032 ,  G06N3/044 ,  G06N3/045 ,  G06N5/022

CPC classification number: G06F40/30 ,  G06N3/044 ,  G06N3/045 ,  G06N5/022 ,  G06F16/90332

Abstract: The present disclosure provides systems and methods that perform machine-learned natural language processing. A computing system can include a machine-learned natural language processing model that includes an encoder model trained to receive a natural language text body and output a knowledge graph and a programmer model trained to receive a natural language question and output a program. The computing system can include a computer-readable medium storing instructions that, when executed, cause the processor to perform operations. The operations can include obtaining the natural language text body, inputting the natural language text body into the encoder model, receiving, as an output of the encoder model, the knowledge graph, obtaining the natural language question, inputting the natural language question into the programmer model, receiving the program as an output of the programmer model, and executing the program on the knowledge graph to produce an answer to the natural language question.

公开(公告)号：US20220171942A1

公开(公告)日：2022-06-02

申请号：US17672364

申请日：2022-02-15

Applicant: Google LLC

Inventor： Ni Lao ,  Jiazhong Nie ,  Fan Yang

IPC: G06F40/30 ,  G06N3/04 ,  G06N5/02

Abstract: The present disclosure provides systems and methods that perform machine-learned natural language processing. A computing system can include a machine-learned natural language processing model that includes an encoder model trained to receive a natural language text body and output a knowledge graph and a programmer model trained to receive a natural language question and output a program. The computing system can include a computer-readable medium storing instructions that, when executed, cause the processor to perform operations. The operations can include obtaining the natural language text body, inputting the natural language text body into the encoder model, receiving, as an output of the encoder model, the knowledge graph, obtaining the natural language question, inputting the natural language question into the programmer model, receiving the program as an output of the programmer model, and executing the program on the knowledge graph to produce an answer to the natural language question.

公开(公告)号：US11256866B2

公开(公告)日：2022-02-22

申请号：US16069781

申请日：2017-10-25

Applicant: Google LLC

Inventor： Ni Lao ,  Jiazhong Nie ,  Fan Yang

IPC: G06F40/30 ,  G06N3/04 ,  G06N5/02 ,  G06F16/9032

Abstract: The present disclosure provides systems and methods that perform machine-learned natural language processing. A computing system can include a machine-learned natural language processing model that includes an encoder model trained to receive a natural language text body and output a knowledge graph and a programmer model trained to receive a natural language question and output a program. The computing system can include a computer-readable medium storing instructions that, when executed, cause the processor to perform operations. The operations can include obtaining the natural language text body, inputting the natural language text body into the encoder model, receiving, as an output of the encoder model, the knowledge graph, obtaining the natural language question, inputting the natural language question into the programmer model, receiving the program as an output of the programmer model, and executing the program on the knowledge graph to produce an answer to the natural language question.

公开(公告)号：US20190130251A1

公开(公告)日：2019-05-02

申请号：US16176961

申请日：2018-10-31

Applicant: Google LLC

Inventor： Ni Lao ,  Chen Liang ,  Quoc V. Le ,  John Blitzer

IPC: G06N3/04 ,  G06F17/30 ,  G06F17/27 ,  G06K9/62

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating a system output from a system input using a neural network system comprising an encoder neural network configured to, for each of a plurality of encoder time steps, receive an input sequence comprising a respective question token, and process the question token at the encoder time step to generate an encoded representation of the question token, and a decoder neural network configured to, for each of a plurality of decoder time steps, receive a decoder input, and process the decoder input and a preceding decoder hidden state to generate an updated decoder hidden state.