公开(公告)号：US20180268298A1

公开(公告)日：2018-09-20

申请号：US15853570

申请日：2017-12-22

Applicant: salesforce.com, inc.

Inventor： Alexander Rosenberg Johansen ,  Bryan McCann ,  James Bradbury ,  Richard Socher

IPC: G06N3/08 ,  G06N3/04 ,  G06K9/62 ,  G06F17/24 ,  G06F17/27 ,  G06N5/04 ,  G06F15/18

CPC classification number: G06N3/0454 ,  G06F15/76 ,  G06F17/241 ,  G06F17/2785 ,  G06K9/6262 ,  G06K9/6267 ,  G06K9/6268 ,  G06K9/6271 ,  G06N3/0445 ,  G06N3/0472 ,  G06N3/0481 ,  G06N3/084 ,  G06N5/04 ,  G06N20/00

Abstract: The technology disclosed proposes using a combination of computationally cheap, less-accurate bag of words (BoW) model and computationally expensive, more-accurate long short-term memory (LSTM) model to perform natural processing tasks such as sentiment analysis. The use of cheap, less-accurate BoW model is referred to herein as “skimming”. The use of expensive, more-accurate LSTM model is referred to herein as “reading”. The technology disclosed presents a probability-based guider (PBG). PBG combines the use of BoW model and the LSTM model. PBG uses a probability thresholding strategy to determine, based on the results of the BoW model, whether to invoke the LSTM model for reliably classifying a sentence as positive or negative. The technology disclosed also presents a deep neural network-based decision network (DDN) that is trained to learn the relationship between the BoW model and the LSTM model and to invoke only one of the two models.

公开(公告)号：US20220164635A1

公开(公告)日：2022-05-26

申请号：US17670368

申请日：2022-02-11

Applicant: salesforce.com, inc.

Inventor： Alexander Rosenberg Johansen ,  Bryan McCann ,  James Bradbury ,  Richard Socher

IPC: G06N3/04 ,  G06K9/62 ,  G06N20/00 ,  G06F15/76 ,  G06F40/30 ,  G06F40/169 ,  G06N3/08 ,  G06N5/04

Abstract: The technology disclosed proposes using a combination of computationally cheap, less-accurate bag of words (BoW) model and computationally expensive, more-accurate long short-term memory (LSTM) model to perform natural processing tasks such as sentiment analysis. The use of cheap, less-accurate BoW model is referred to herein as “skimming”. The use of expensive, more-accurate LSTM model is referred to herein as “reading”. The technology disclosed presents a probability-based guider (PBG). PBG combines the use of BoW model and the LSTM model. PBG uses a probability thresholding strategy to determine, based on the results of the BoW model, whether to invoke the LSTM model for reliably classifying a sentence as positive or negative. The technology disclosed also presents a deep neural network-based decision network (DDN) that is trained to learn the relationship between the BoW model and the LSTM model and to invoke only one of the two models.

公开(公告)号：US11354565B2

公开(公告)日：2022-06-07

申请号：US15853530

申请日：2017-12-22

Applicant: salesforce.com, inc.

Inventor： Alexander Rosenberg Johansen ,  Bryan McCann ,  James Bradbury ,  Richard Socher

IPC: G06N3/04 ,  G06N20/00 ,  G06F40/30 ,  G06K9/62 ,  G06F15/76 ,  G06F40/169 ,  G06N3/08 ,  G06N5/04

Abstract: The technology disclosed proposes using a combination of computationally cheap, less-accurate bag of words (BoW) model and computationally expensive, more-accurate long short-term memory (LSTM) model to perform natural processing tasks such as sentiment analysis. The use of cheap, less-accurate BoW model is referred to herein as “skimming”. The use of expensive, more-accurate LSTM model is referred to herein as “reading”. The technology disclosed presents a probability-based guider (PBG). PBG combines the use of BoW model and the LSTM model. PBG uses a probability thresholding strategy to determine, based on the results of the BoW model, whether to invoke the LSTM model for reliably classifying a sentence as positive or negative. The technology disclosed also presents a deep neural network-based decision network (DDN) that is trained to learn the relationship between the BoW model and the LSTM model and to invoke only one of the two models.

公开(公告)号：US20180268287A1

公开(公告)日：2018-09-20

申请号：US15853530

申请日：2017-12-22

Applicant: salesforce.com, inc.

Inventor： Alexander Rosenberg Johansen ,  Bryan McCann ,  James Bradbury ,  Richard Socher

IPC: G06N3/04 ,  G06K9/62

CPC classification number: G06N3/0454 ,  G06F15/76 ,  G06F17/241 ,  G06F17/2785 ,  G06K9/6262 ,  G06K9/6267 ,  G06K9/6268 ,  G06K9/6271 ,  G06N3/0445 ,  G06N3/0472 ,  G06N3/0481 ,  G06N3/084 ,  G06N5/04 ,  G06N20/00

Abstract: The technology disclosed proposes using a combination of computationally cheap, less-accurate bag of words (BoW) model and computationally expensive, more-accurate long short-term memory (LSTM) model to perform natural processing tasks such as sentiment analysis. The use of cheap, less-accurate BoW model is referred to herein as “skimming”. The use of expensive, more-accurate LSTM model is referred to herein as “reading”. The technology disclosed presents a probability-based guider (PBG). PBG combines the use of BoW model and the LSTM model. PBG uses a probability thresholding strategy to determine, based on the results of the BoW model, whether to invoke the LSTM model for reliably classifying a sentence as positive or negative. The technology disclosed also presents a deep neural network-based decision network (DDN) that is trained to learn the relationship between the BoW model and the LSTM model and to invoke only one of the two models.

公开(公告)号：US12235790B2

公开(公告)日：2025-02-25

申请号：US17670368

申请日：2022-02-11

Applicant: salesforce.com, inc.

Inventor： Alexander Rosenberg Johansen ,  Bryan McCann ,  James Bradbury ,  Richard Socher

IPC: G06N20/00 ,  G06F15/76 ,  G06F18/21 ,  G06F18/24 ,  G06F18/241 ,  G06F18/2413 ,  G06F40/169 ,  G06F40/30 ,  G06N3/04 ,  G06N3/044 ,  G06N3/045 ,  G06N3/047 ,  G06N3/048 ,  G06N3/08 ,  G06N3/084 ,  G06N5/04 ,  G06V10/764 ,  G06V10/776 ,  G06V10/82

Abstract: The technology disclosed proposes using a combination of computationally cheap, less-accurate bag of words (BoW) model and computationally expensive, more-accurate long short-term memory (LSTM) model to perform natural processing tasks such as sentiment analysis. The use of cheap, less-accurate BoW model is referred to herein as “skimming”. The use of expensive, more-accurate LSTM model is referred to herein as “reading”. The technology disclosed presents a probability-based guider (PBG). PBG combines the use of BoW model and the LSTM model. PBG uses a probability thresholding strategy to determine, based on the results of the BoW model, whether to invoke the LSTM model for reliably classifying a sentence as positive or negative. The technology disclosed also presents a deep neural network-based decision network (DDN) that is trained to learn the relationship between the BoW model and the LSTM model and to invoke only one of the two models.

公开(公告)号：US11250311B2

公开(公告)日：2022-02-15

申请号：US15853570

申请日：2017-12-22

Applicant: salesforce.com, inc.

Inventor： Alexander Rosenberg Johansen ,  Bryan McCann ,  James Bradbury ,  Richard Socher

IPC: G06N3/04 ,  G06K9/62 ,  G06N20/00 ,  G06F15/76 ,  G06F40/30 ,  G06F40/16 ,  G06N3/08 ,  G06N5/04 ,  G06F40/169

Abstract: The technology disclosed proposes using a combination of computationally cheap, less-accurate bag of words (BoW) model and computationally expensive, more-accurate long short-term memory (LSTM) model to perform natural processing tasks such as sentiment analysis. The use of cheap, less-accurate BoW model is referred to herein as “skimming”. The use of expensive, more-accurate LSTM model is referred to herein as “reading”. The technology disclosed presents a probability-based guider (PBG). PBG combines the use of BoW model and the LSTM model. PBG uses a probability thresholding strategy to determine, based on the results of the BoW model, whether to invoke the LSTM model for reliably classifying a sentence as positive or negative. The technology disclosed also presents a deep neural network-based decision network (DDN) that is trained to learn the relationship between the BoW model and the LSTM model and to invoke only one of the two models.