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.

公开(公告)号：US20220083819A1

公开(公告)日：2022-03-17

申请号：US17457163

申请日：2021-12-01

Applicant: salesforce.com, inc.

Inventor： Ankit Chadha ,  Caiming Xiong ,  Ran Xu

IPC: G06K9/62 ,  G06T3/40 ,  G06T3/60 ,  G06N3/04 ,  G06N3/08 ,  G06T3/20

Abstract: Computing systems may support image classification and image detection services, and these services may utilize object detection/image classification machine learning models. The described techniques provide for normalization of confidence scores corresponding to manipulated target images and for non-max suppression within the range of confidence scores for manipulated images. In one example, the techniques provide for generating different scales of a test image, and the system performs normalization of confidence scores corresponding to each scaled image and non-max suppression per scaled image These techniques may be used to provide more accurate image detection (e.g., object detection and/or image classification) and may be used with models that are not trained on modified image sets. The model may be trained on a standard (e.g. non-manipulated) image set but used with manipulated target images and the described techniques to provide accurate object detection.