公开(公告)号：US12086548B2

公开(公告)日：2024-09-10

申请号：US17039919

申请日：2020-09-30

Applicant: Amazon Technologies, Inc.

Inventor： Rishita Rajal Anubhai ,  Yahor Pushkin ,  Graham Vintcent Horwood ,  Yinxiao Zhang ,  Ravindra Manjunatha ,  Jie Ma ,  Alessandra Brusadin ,  Jonathan Steuck ,  Shuai Wang ,  Sameer Karnik ,  Miguel Ballesteros Martinez ,  Sunil Mallya Kasaragod ,  Yaser Al-Onaizan

IPC: G06F40/30 ,  G06F40/295 ,  G06N20/00

CPC classification number: G06F40/30 ,  G06F40/295 ,  G06N20/00

Abstract: Methods, systems, and computer-readable media for event extraction from documents with co-reference are disclosed. An event extraction service identifies one or more trigger groups in a document comprising text. An individual one of the trigger groups comprises one or more textual references to an occurrence of an event. The one or more trigger groups are associated with one or more semantic roles for entities. The event extraction service identifies one or more entity groups in the document. An individual one of the entity groups comprises one or more textual references to a real-world object. The event extraction service assigns one or more of the entity groups to one or more of the semantic roles. The event extraction service generates an output indicating the one or more trigger groups and one or more entity groups assigned to the semantic roles.

公开(公告)号：US11861039B1

公开(公告)日：2024-01-02

申请号：US17035437

申请日：2020-09-28

Applicant: Amazon Technologies, Inc.

Inventor： Yahor Pushkin ,  Sravan Babu Bodapati ,  Sunil Mallya Kasaragod ,  Sameer Karnik ,  Abhinav Goyal ,  Yaser Al-Onaizan ,  Ravindra Manjunatha ,  Kalpit Dixit ,  Alok Kumar Parmesh ,  Syed Kashif Hussain Shah

IPC: G06F21/62 ,  G06F16/903 ,  G06F3/06 ,  G06N20/00

CPC classification number: G06F21/6245 ,  G06F3/0619 ,  G06F3/0623 ,  G06F3/0683 ,  G06F16/90344 ,  G06N20/00

Abstract: Various embodiments of a hierarchical system or method of identifying sensitive content in data is described. In some embodiments, sensitive data classifiers local to a data storage system can analyze a plurality of data items and classify at least some data items as potentially containing sensitive data. The sensitive data classifiers can provide the classified data items to a separate sensitive data discovery component. The sensitive data discovery component can, in some embodiments, obtain the classified data items, perform a sensitive data location analysis on the classified data items to identify a location of sensitive data within some of the classified data items, and generate location information for the sensitive data within the data items containing sensitive data. The sensitive data discovery component can provide to a destination this information, in some embodiments, where the destination might redact, tokenize, highlight, or perform other actions on the located sensitive data.

公开(公告)号：US11741168B1

公开(公告)日：2023-08-29

申请号：US16588595

申请日：2019-09-30

Applicant: Amazon Technologies, Inc.

Inventor： Sravan Babu Bodapati ,  Rishita Rajal Anubhai ,  Yahor Pushkin

IPC: G06F16/93 ,  G06N5/04 ,  G06N20/00

CPC classification number: G06F16/93 ,  G06N5/04 ,  G06N20/00

Abstract: Techniques for multi-label document classification are described. Clustering is used to cluster labels in a set. A machine learning model including a multi-label classifier for each cluster is created, the multi-label classifier for a given cluster to classify a document with one or more of the labels in the cluster.

公开(公告)号：US20220100967A1

公开(公告)日：2022-03-31

申请号：US17039891

申请日：2020-09-30

Applicant: Amazon Technologies, Inc.

Inventor： Yahor Pushkin ,  Rishita Rajal Anubhai ,  Sameer Karnik ,  Sunil Mallya Kasaragod ,  Abhinav Goyal ,  Yaser Al-Onaizan ,  Ashish Singh ,  Ashish Khare

IPC: G06F40/35 ,  G06K9/62 ,  G06F40/295

Abstract: Methods, systems, and computer-readable media for lifecycle management for customized natural language processing are disclosed. A natural language processing (NLP) customization service determines a task definition associated with an NLP model based (at least in part) on user input. The task definition comprises an indication of one or more tasks to be implemented using the NLP model and one or more requirements associated with use of the NLP model. The service determines the NLP model based (at least in part) on the task definition. The service trains the NLP model. The NLP model is used to perform inference for a plurality of input documents. The inference outputs a plurality of predictions based (at least in part) on the input documents. Inference data is collected based (at least in part) on the inference. The service generates a retrained NLP model based (at least in part) on the inference data.