Learning Objectives: - Get an introduction to Natural Language Processing (NLP) - Learn benefits of new approaches to analytics and technologies that help empower better decisions, e.g., NLP, data prep - Build a text analytics solution with Amazon Comprehend and Amazon Relational Database Service in a step by step demo

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Nino Bice, Product Manager
Ben Snively, Service Architect
Amazon Comprehend
Fully Managed, Continuously Trained NLP Service

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Frameworks
&
Infrastructure
AWS Deep Learning AMI
GPU
(P3 Instances)
MobileCPU
IoT
(Greengrass)
Vision:
Rekognition Image
Rekognition Video
Speech:
Polly
Transcribe
Language:
Lex Translate
Comprehend
Apache
MXNet
PyTorch
Cognitive
Toolkit
Keras
Caffe2
& Caffe
TensorFlow Gluon
Application
Services
Platform
Services
Amazon Machine
Learning
Mechanical
Turk
Spark &
EMR
Amazon
SageMaker
AWS
DeepLens
Amazon confidential
AWS ML Stack

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Amazon Transcribe
Automatic conversion of speech into accurate, grammatically
correct text
Amazon Translate Natural and fluent language translation
Amazon Polly Turn text into lifelike speech using deep learning
Amazon Comprehend Discover insights and relationships in text
Amazon Lex
Conversational interfaces for text-based and voice-based
applications
Powerful language capabilities

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Amazon Comprehend
A fully managed and continuously trained service that
helps you extract insights from unstructured text

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Amazon Comprehend
Sentiment Entities LanguagesKey phrases Topic modeling

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Amazon Comprehend: Natural Language Processing
A m a z o n . c o m , I n c . i s l o c a t e d i n
S e a t t l e , WA a n d w a s f o u n d e d
J u l y 5 t h , 1 9 9 4 b y J e f f B e z o s .
O u r c u s t o m e r s l o v e b u y i n g
e v e r y t h i n g f r o m b o o k s t o
b l e n d e r s a t g r e a t p r i c e s
N a m e d E n t i t i e s
• A m a z o n . c o m :
O r g a n i z a t i o n
• S e a t t l e , W A : L o c a t i o n
• J u l y 5 t h , 1 9 9 4 : D a t e
• J e f f B e z o s : P e r s o n
K e y p h r a s e s
• O u r c u s t o m e r s
• b o o k s
• b l e n d e r s
• g r e a t p r i c e s
S e n t i m e n t
• P o s i t i v e
L a n g u a g e
• E n g l i s h

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Topic Modeling
Document Topic Proportion
Doc.txt 0 .89
Doc.txt 1 .67
Doc.txt 2 .91
Topic Term Weight
0 Washington .89
1 Silicon Valley .67
2 Roasting .91
Keywords Topic Groups Document Relationship to Topics

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Data
Collection
Web
Crawling
Internal
Data
Warehouse
Purchase
Datasets
Annotation
Orchestration
Data
Cleansing
Annotation Evaluation
Model
Training
Build Experiment Optimize
Deploy Dockerize Deploy Host
Continuous
Improvement
Sample
Data
Annotate
and
Evaluate
Repeat
Steps 3 - 5
1.
2.
3.
4.
5.
Continuously Training

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Comprehend Portal

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AWS Text Analytics

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Popular Text Analytics Use Cases
Content Personalization: Customers are using Comprehend NLP output to understand related documents
based on entities, phrases or even topic similarities for trends analysis, to drive content personalization and
recommendations
Semantic Search: Customers using Amazon Comprehend to index entities for boosting and ranking
search results.
Intelligent data warehouse: Customers are using Amazon Comprehend to query unstructured data in
relational databases, processing data within the data lake (S3) and then inserting it back into the data
warehouse
Social Analytics: Customers are using Amazon Comprehend to ingest, process and analyze trends from
entities and sentiment from social media posts across Twitter and Facebook.

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AWS Text Analytics Workload
Amazon Kinesis
Amazon ES
Amazon Redshift
Amazon EMR
• Semantic
• Rich Filtering
• Grouping, Trends
• Joining, Correlating
• Clustering
• Graph, Search
• Near real-time
• Alerts
Amazon S3
Social Media, Support
Amazon Aurora
Articles, Documents

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Comprehend and Glue

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UDF to Call Comprehend

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Comprehend and RDS

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Stored Procedure to Comprehend

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Demo: Comprehend and RDS

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Comprehend and Elasticsearch

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Comprehend and SageMaker

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