With the advent of bot frameworks and voice-only devices, we are seeing a change with how humans interact with computers. Instead of humans learning computer interfaces—which can be largely non-intuitive—computers are instead learning how humans innately communicate with language. Powering this change is the field of Natural Language Processing (NLP). I will first introduce NLP and discuss its various approaches. I will then cover current challenges in the field such as handling content containing domain specific jargon and the techniques to tackle those challenges such as Deep Learning. Lastly, I will show how to start using NLP technology for content understanding / generation with existing API ecosystems. This presentation was given at Information Development World on May 17, 2017 Don’t miss our upcoming event, Information Development World: Creating Machine-Ready Content to be held on November 28-30, 2017. http://www.informationdevelopmentworld.com

1. Getting Started With
Natural Language Processing
Jason Anderson Comp Three Inc. May 17th 2017

3. Agenda
1. Start Using NLP Today
2. General NLP Use Cases
3. NLP Adoption Onramps

4. Presenter Background
• Undergraduate and graduate work in Machine Learning
• Started out building IBM Cloud solutions
• Working with IBM to build solutions using their Watson Services
• Helping various clients adopt Machine Learning / NLP

5. First Play with NLP
The best way to start is by playing with NLP demos:
• Stanford CoreNLP Demo
• Text Classification (source)
• Image Caption Bot Demo

7. Sample NLP Use Cases
1. Text Generation (e.g. captioning)
2. Unlocking "Dark" Data
3. Conversational Interfaces

8. Use Case: Unlocking "Dark" Data
NLP Technology
{
"text": "Cable News Network",
"type": "Organization",
"relevance": "0.906502",
"sentiment": "neutral",
}, {
"type": "Organization",
"relevance": "0.693066",
"sentiment": "neutral"
"text": "Turner Broadcasting System"
}, {
"type": "Person",
"relevance": "0.651281",
"sentiment": "neutral"
"text": "Ted Turner",
},
Unstructured
"Dark" Data
Structured and
Annotated Data
The Cable News Network is an American
basic cable and satellite television news
channel owned by the Turner Broadcasting
System division of Time Warner. It was
founded in 1980 by American media
proprietor Ted Turner as a 24-hour cable
news channel.
https://alchemy-language-demo.mybluemix.net/

9. Use Case: Conversational Interfaces
http://medbot.compthree.com/

10. What makes NLP challenging?
We see…
How are you doing today?

11. What makes NLP challenging?
We see…
How are you doing today?
Computers see…
01001000 01101111 01110111 00100000 01100001
01110010 01100101 00100000 01111001 01101111
01110101 00100000 01110100 01101111 01100100
01100001 01111001 00111111

12. Approaches To Adopting NLP
1. Use OpenSource Technology
2. Leverage Proprietary APIs
3. Work with an Expert
Good
Fast Cheap
Choose two of the above

13. 1. Open Source Technology
There are a number of open source projects available for NLP
Examples:
• NLTK (python)
• spacey.io (python)
• NLP Compromise (NodeJS)
You can even use Deep Learning!
• Using a word embedding technology like Word2Vec you can leverage Neutral Network
techniques

14. 1. Open Source Technology
Pros
• Low technology costs
• Most flexibility in building a solution
Cons
• Requires the most internal skills investment
• Greatest time investment for complete project

15. 2. Leverage Proprietary APIs
• Many companies focus on providing productized NLP technologies
• Most of these technologies are available through APIs so they can be
easily consumed from any programing language
• Some of the services have SDKs which make the service easier to use

16. 2. Leverage Proprietary APIs
https://console.ng.bluemix.net/catalog/

17. 2. Leverage Proprietary APIs
Request
curl -X POST -u "{username}":"{password}" -H "Content-Type:application/json" -d "{"text":"How hot will it be today?"}"
"https://gateway.watsonplatform.net/natural-language-classifier/api/v1/classifiers/10D41B-nlc-1/classify"
Response
{
"classifier_id": "10D41B-nlc-1",
"url": "https://gateway.watsonplatform.net/natural-language-classifier/api/v1/classifiers/10D41B-nlc-
1/classify?text=How%20hot%20wil/10D41B-nlc-1",
"text": "How hot will it be today?",
"top_class": "temperature",
"classes": [
{
"class_name": "temperature",
"confidence": 0.9998201258549781
},
{
"class_name": "conditions",
"confidence": 0.00017987414502176904
}
]
}

18. 2. Leverage Proprietary APIs
Pros
• Lower skills investment for in-house developers
• APIs prescribe a certain approach with documentation
• Most gotchas and best practices are baked into the service
Cons
• Still requires some internal machine learning skills
• API costs can be large at scale
• Requires Internet connectivity for API access

19. 3. Work with an Expert
• Build a solution along side someone who has built these types of
solutions before
• Experts can also be consulted for advice to ensure you’re proceeding
down the correct path (roadmapping)
• Ask what previous experience an expert has to evaluate their
applicability to your domain

20. 3. Work with an Expert
Pros
• Quickest way to realize value
• Solution customized to your organization
Cons
• With in-demand skills come in-demand prices

21. Summary
Get started with NLP today:
1. The What - Play with NLP demos to understand its capabilities
2. The Why - Think about the value your organization can realize using
NLP (which use cases apply?)
3. The How - Determine which NLP adoption onramp is right for you
(might be a combination)

22. Questions?
Jason Anderson - janderson@compthree.com