公开(公告)号：US20230086302A1

公开(公告)日：2023-03-23

申请号：US17479748

申请日：2021-09-20

Applicant: salesforce.com, inc.

Inventor： Shilpa Bhagavath ,  Shubham Mehrotra ,  Abhishek Sharma ,  Shashank Harinath ,  Na Cheng ,  Zineb Laraki

IPC: G06F40/263 ,  G06F40/58 ,  G06K9/62

Abstract: A method that includes receiving an input at an interactive conversation service that uses an intent classification model. The method may further include generating, using an encoder model of the intent classification model, a set of output vectors corresponding to the input, where the encoder model is configured to determine a set of metrics corresponding to intent classifications. The method may further include determining, using an outlier detection model of the intent classification model, whether the input is in-domain or out-of-domain (OOD) based on a first vector of the set of output vectors satisfying a domain threshold relative to one or more of the intent classifications. The method may further include outputting, by the intent classification model, a second vector of the set of output vectors that indicates the set of metrics corresponding to the intent classifications or an indication that the input is OOD.

公开(公告)号：US11436481B2

公开(公告)日：2022-09-06

申请号：US16134957

申请日：2018-09-18

Applicant: salesforce.com, inc.

Inventor： Govardana Sachithanandam Ramachandran ,  Michael Machado ,  Shashank Harinath ,  Linwei Zhu ,  Yufan Xue ,  Abhishek Sharma ,  Jean-Marc Soumet ,  Bryan McCann

IPC: G06N3/08 ,  G06F16/35 ,  G06F40/00

Abstract: A method for natural language processing includes receiving, by one or more processors, an unstructured text input. An entity classifier is used to identify entities in the unstructured text input. The identifying the entities includes generating, using a plurality of sub-classifiers of a hierarchical neural network classifier of the entity classifier, a plurality of lower-level entity identifications associated with the unstructured text input. The identifying the entities further includes generating, using a combiner of the hierarchical neural network classifier, a plurality of higher-level entity identifications associated with the unstructured text input based on the plurality of lower-level entity identifications. Identified entities are provided based on the plurality of higher-level entity identifications.

公开(公告)号：US20220222489A1

公开(公告)日：2022-07-14

申请号：US17202188

申请日：2021-03-15

Applicant: salesforce.com, inc.

Inventor： Jingyuan Liu ,  Abhishek Sharma ,  Suhail Sanjiv Barot ,  Gurkirat Singh ,  Mridul Gupta ,  Shiva Kumar Pentyala ,  Ankit Chadha

IPC: G06K9/62 ,  G06F40/295 ,  G06F40/247 ,  G06F40/35 ,  G06F40/284 ,  G06N20/00

Abstract: A system performs named entity recognition for performing natural language processing, for example, for conversation engines. The system uses context information in named entity recognition. The system includes the context of a sentence during model training and execution. The system generates high quality contextual data for training NER models. The system utilizes labeled and unlabeled contextual data for training NER models. The system provides NER models for execution in production environments. The system uses heuristics to determine whether to use a context-based NER model or a simple NER model that does not use context information. This allows the system to use simple NER models when the likelihood of improving the accuracy of prediction based on context is low.

公开(公告)号：US20220222441A1

公开(公告)日：2022-07-14

申请号：US17202183

申请日：2021-03-15

Applicant: salesforce.com, inc.

Inventor： Jingyuan Liu ,  Abhishek Sharma ,  Suhail Sanjiv Barot ,  Gurkirat Singh ,  Mridul Gupta ,  Shiva Kumar Pentyala ,  Ankit Chadha

IPC: G06F40/295 ,  G06F40/35 ,  G06F40/247 ,  G06N3/08

Abstract: A system performs named entity recognition for performing natural language processing, for example, for conversation engines. The system uses context information in named entity recognition. The system includes the context of a sentence during model training and execution. The system generates high quality contextual data for training NER models. The system utilizes labeled and unlabeled contextual data for training NER models. The system provides NER models for execution in production environments. The system uses heuristics to determine whether to use a context-based NER model or a simple NER model that does not use context information. This allows the system to use simple NER models when the likelihood of improving the accuracy of prediction based on context is low.