Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Chatbots and Voice Conversational Interfaces with Amazon Alexa, Neo4j and GraphAware NLP

7,930 views

Published on

The age of touch could soon come to an end. From smartphones and smartwatches, to home devices, in-car systems, touch is no longer the primary user interface. (source: Design News)

During this talk, Christophe, Principal Consultant at GraphAware will walk you through the design of building Conversational Bots. To this end, he used Amazon Alexa and combined it with a Natural Language Processing stack backed by a Neo4j Graph Database.

You will discover the basics of an Amazon Alexa skill and how the user experience with voice devices can be enhanced with graph based algorithms such as recommendations.

Published in: Technology
  • Be the first to comment

Chatbots and Voice Conversational Interfaces with Amazon Alexa, Neo4j and GraphAware NLP

  1. 1. Hello humans !
  2. 2. Forget everything !
  3. 3. Everything you know…
  4. 4. Everything you learned…
  5. 5. is OVER !!!
  6. 6. Welcome to a brand new world…
  7. 7. The world of VOICE
  8. 8. From Alexa
  9. 9. To Google Home,
  10. 10. In-car systems,
  11. 11. and smartwatches
  12. 12. Touch is no longer the primary UI
  13. 13. The touch era is OVER !
  14. 14. Chatbot and Conversational Experiences with Amazon Alexa, Neo4j and GraphAware NLP GraphDB Meetup Czech Republic June 14th, 2017 Christophe Willemsen - @ikwattro
  15. 15. Program of tonight • Chatbots history • Voice-driven chatbot anatomy • Let’s build our conference assistant • Introduction to Natural Language Processing • Let’s chat..bot
  16. 16. Chatbots history • Alan Turing Test
  17. 17. Chatbots history • Eliza (1966) mimicked human conversations by matching user prompts to scripted responses. It was able, at least for a time, to pass the Turing test
  18. 18. Chatbots history • Parry (1972) – Inexplicably simulated a person with paranoid schyzophrenia. Parry was more advanced than Eliza.
  19. 19. Chatbots history • 1988 – Jabberwacky • 1992 - Dr. Sbaitso • 1995 – A.L.I.C.E • 2001 – Smarterchild • 2006 – IBM’s Watson • 2010 – SIRI • 2012 – Google NOW • 2016 – Bots for Messenger, TAY • And Alexa ? Ask her !
  20. 20. Chatbot anatomy • Voice Commands • Intent detection • Intent handling • Response
  21. 21. Our conference assistant • Tell me how many sessions ? • Tell me how many sessions about a specific topic ? • Recommend me a session based on my topic preferences • Find me a session about a specific topic with NLP Alexa should be able to :
  22. 22. Dataset used Graphconnect London 2017 schedule
  23. 23. Voice command • Give a name to our Alexa skill -> CONFERENCE ASSISTANT • Define our first intent name (code) -> talksCount • Define our first utterance
  24. 24. ALEXA, ASK CONFERENCE ASSISTANT (skill invocation) HOW MANY SESSIONS TODAY ? (skill utterance) INTENT DETECTION SPEECH TO TEXT TEXT TO SPEECH LAMBDA FUNCTION OR YOUR API SERVER ALEXA VOICE SERVICE
  25. 25. Utterances • How many sessions ? • How much sessions ? • How many talks at the conference ? • How many talks today ?
  26. 26. Intent detection • 2 main types of detection : -> Pattern matching (how | count)* many (session | talks) today? -> Classification machine learning, neural networks, word2vec, ..
  27. 27. Alexa send to the skill API
  28. 28. Your skill returns a text response
  29. 29. Intent detection • 2 main types of detection : -> Pattern matching (how | count)* many (session | talks) today? -> Classification machine learning, neural networks, word2vec, ..
  30. 30. First intent test ALEXA, HOW MANY SESSIONS TODAY ?
  31. 31. ALEXA, ASK CONFERENCE ASSISTANT (skill invocation) HOW MANY SESSIONS TODAY ? (skill utterance) INTENT DETECTION SPEECH TO TEXT TEXT TO SPEECH LAMBDA FUNCTION OR YOUR API SERVER ALEXA VOICE SERVICE
  32. 32. Slots • Variables in the utterances • Should be filled with possible values • Slots are sent in the intent request to your API
  33. 33. Slots HOW MANY SESSIONS ABOUT {topic}
  34. 34. Second intent test ALEXA, HOW MANY SESSIONS ABOUT PERFORMANCE ?
  35. 35. Adding personalization • If you have informations about the attendees and their topic preferences, you can easily get the sessions in accordance with it.
  36. 36. Adding personalization
  37. 37. Adding personalization
  38. 38. Third intent test ALEXA, RECOMMEND ME A SESSION
  39. 39. GraphAware NLP • Graph based Natural Language Processing framework • Information Extraction, Sentiment Analysis, Data enrichment (ontologies and concepts), similiraty computation, knowledge enrichment, … • Beta program, first public open-source version to be released very soon
  40. 40. Enriching with concepts
  41. 41. Enriching with concepts
  42. 42. Tackling the last intent • Question / response game • Treat question as text and process NLP • Relate question text with Session abstracts texts • Special type of utterance with only one slot {text}
  43. 43. Demo
  44. 44. Fun / Ideas with Alexa
  45. 45. Resources • https://github.com/alexylem/jarvis
  46. 46. Thank you ! Questions? graphaware.com @graph_aware

×