Demonstrates how to create a custom persons entity model with Idyl E3 Entity Extraction Engine to improve performance and for secure environments.

1. Creating a Custom Persons Entity Model
with Idyl E3 Entity Extraction Engine
Mountain Fog
Copyright 2017 Mountain Fog, Inc. All Rights Reserved.

2. Introducing Idyl E3
● Performs named-entity extraction from text.
● Capabilities exposed as a REST API.
● Launched easily via the AWS Marketplace.
● Available in multiple editions.
● Learn more at www.mtnfog.com.

3. Entity Models
Idyl E3 uses entity models to extract named
entities.
You can create your own entity models from your
data.
Custom models improve performance and
security.

4. Get Idyl E3
Launch Idyl E3 via the AWS Marketplace.
http://aws.amazon.com/marketplace/pp/B01N11P6V3

5. Annotated Training Data
Training requires annotated training data.
<START:person> George Washington <END> was president.
He was friends with <START:person> Dalton Briscoe <END>.
<START:person> Vaughn Everett <END> and <START:person> Huey Otis <END> are baseball players.
I, <START:person> Dawson Garth <END>, don’t remember that.
<START:person> London Alexander <END> was a country music singer.
One annotated sentence per line.
More data improves training.
Saved as person-train.txt:

6. Create Training Definition File
An XML file that describes the model training.
<?xml version="1.0" encoding="UTF­8"?>
<trainingdefinition xmlns="http://www.mtnfog.com">
<algorithm cutoff="1" iterations="1" />
<trainingdata file="person­train.txt" format="opennlp" />
<model file="/tmp/persons.bin" encryptionkey="enckey" language="eng" type="person" />
<features>
<generators>
<cache>
<generators>
<window prevLength="2" nextLength="2">
<tokenclass />
</window>
<window prevLength="2" nextLength="2">
<token />
</window>
<sentence begin="true" end="true" />
</generators>
</cache>
</generators>
</features>
</trainingdefinition>
Saved as training-definition.xml:

7. Create Persons Entity Model
# SSH to the Idyl E3 instance.
ssh ­i <key> ec2­user@<Public_Ip>
# Train the model.
cd /opt/idyl­e3/bin
./train­entity­model.sh training­definition.xml
# Copy the generated model and its manifest.
cp /tmp/model.bin ../models/
cp /tmp/model.manifest ../models/
# Restart Idyl E3 to use the model.
sudo service idyl­e3 restart

8. Using the Entity Model
Idyl E3 will now use the new entity model to
extract entities when requests are received.

9. Entity Extraction Response
{
"entities":[
{
"text":"George Washington",
"confidence":0.96,
"span":{
"tokenStart":0,
"tokenEnd":2,
"characterStart":0,
"characterEnd":17
},
"type":"person",
"languageCode":"eng",
"extractionDate":1504022495052,
"metadata":{
}
}
],
"extractionTime":48
}
An example entity extraction request and response:
Response
curl http://<Public_IP>:9000/api/extract
­d “George Washington was president.”
Entity Extraction Request

10. Summary
● Idyl E3 extracts entities from natural language
text.
● You can create entity models from your own text
to extract entities.
– Improves model performance.
– Useful for secure environments where text cannot
leave the local network.
● Idyl E3 can be quickly launched from the
AWS Marketplace.

11. support@mtnfog.com
www.mtnfog.com