Amazon Technologies, Inc.

Yahor Pushkin

Sunil Mallya Kasaragod

Yaser Al-Onaizan

Sravan Babu Bodapati

Rishita Rajal Anubhai

Miguel Ballesteros Martinez

Sameer Karnik

Graham Vintcent Horwood

Abhinav Goyal

Rama Krishna Sandeep Pokkunuri

Ganesh Kumar Gella

Roger Scott Jenke

Harshal Pimpalkhute

Vasanth Philomin

Yogarshi Paritosh Vyas

Ravindra Manjunatha

Srikanth Doss Kadarundalagi Raghura

Yinxiao Zhang

Swaminathan Sivasubramanian

Yi Zhang

Yusheng Xie

Swapandeep Singh

Saman Zarandioon

Meghana Puvvadi

Dimitrios Soulios

Manikya Pavan Kiran Pothukuchi

Saab Mansour

Kasturi Bhattacharjee

Syed Kashif Hussain Shah

Kalpit Dixit

Smaranda Muresan

Shuai Wang

Salvatore Romeo

Kapil Singh Badesara

Jie Ma

Santosh Kumar Ameti

Bo Pang

Alok Kumar Parmesh

Alessandra Brusadin

Jonathan Steuck

Federico Torreti

Siddharth Chaitanyakumar Varia

Aruni Roy Chowdhury

Liutong Zhou

Xuan Zhu

Diego Marcheggiani

Methods, systems, and computer-readable media for context-sensitive linking of entities to private databases are disclosed. An entity linking service stores a plurality of representations of entities. Individual ones of the entities correspond to individual ones of a plurality of records in one or more private databases. The entity linking service determines a mention of an entity in a document. The entity linking service selects, from the plurality of records in the one or more private databases, a record corresponding to the entity. The record is selected based at least in part on the plurality of representations of the entities and based at least in part on a context of the mention of the entity in the document. The entity linking service generates output comprising a reference to the selected record in the one or more private databases.

Context-sensitive linking of entities to private databases

Techniques for utilizing a federated learning service are described. An exemplary method includes causing a development of a deployable machine learning model using at least two devices, the development of the deployable machine learning model including: providing an initial machine learning model or algorithm to the at least two devices external to the provider network, causing each of the at least two devices external to the provider network to locally train the initial machine learning model or algorithm using training data to each generate a modified version of the initial machine learning model, determining updates between the initial model and the generated modified versions of the initial machine learning model, and applying the determined updates to the initial model to generate the candidate machine learning model.

Federated learning service in a provider network and training machine learning models using devices external to the provider network

A system receives one or more transcripts of communications between entities. The system identifies a requested action in the communications based at least in part on a mapping between the requested action and an application programming interface. The system identifies one or more statements eliciting information, based on parameters to the application programming interface. The system generates a definition of an artificial agent based, at least in part, on the requested action and the one more statements eliciting information.

Artificial agent generator

A global segmenting and analysis service of a provider network may receive documents (e.g., posts, product reviews) from different applications. The service may analyze the documents to identify target entities and sentiment. The service may generate different levels of sentiment data and store data into a segmented database. For example, the service may store within-document level sentiment, document-level sentiment, and multi-document level sentiment for a target entity. The service may also update the entity taxonomy automatically or with only a small number of sample documents. The client may query the service for the segmented sentiment data.

Global segmenting and sentiment analysis based on granular opinion detection

Using a first set of machine learning models, a communication from a user of a restaurant is analyzed at an order coordinator linked via a network to resources of an order management service at a provider network. A response to the communication is prepared using another set of models at the provider network and presented to the user. An order of the user for one or more restaurant menu items is fulfilled, based at least partly on analysis of a second communication received from the user after the response is presented.

Distributed system for automated restaurant order acquisition

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.

Event extraction from documents with co-reference

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.

Hierarchical system and method for identifying sensitive content in data

Techniques for performing natural language processing (NLP) on semi-structured data are described. An exemplary method includes receiving a semi-structured document to perform NLP on using a trained NLP model; converting the semi-structured document into a secondary format, wherein the secondary format includes spatial information for tokens of the semi-structured document; flattening the converted, secondary formatted semi-structured document into a Unicode Transformation Format text file; performing NLP on the Unicode Transformation Format text file using the trained NLP model; and providing a result of the NLP to a requester.

Natural language processing on semi-structured data

A data lineage system tracks performance of data flows through different transformations independent of the systems that perform the transformations. A data flow model is maintained as a graph in the data lineage system that is updated by data processors to include performance history of different transformations in the data flow. Subsequent analyses of the data flow model, such as tracing particular data, can be supported using the recorded performance information in the graph of the data flow model.

System-independent data lineage system

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.

Multi-label document classification for documents from disjoint class sets

Techniques for creating a text classifier machine learning (ML) model are described. According to some embodiments, a language processing service finetunes a language ML model on unlabeled documents of a user, and then trains that finetuned language ML model on labeled documents of the user to be a text classifier that is customized for that user's domain, e.g., the user's documents. Additionally, the finetuned language ML model may be trained on labeled documents of the user, for prediction objectives for unlabeled data, before being trained as the text classifier.

Creating text classification machine learning models

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Redmond, WA, United States of America

Yahor Pushkin