Downloaded 12 times
![Frequency vectors
The quick brown fox jumps over the lazy dog
[1, 1, 1, 1, 1, 0, 1, 1]
Never jump over the lazy dog quickly
[0, 1, 0, 1, 1, 1, 1, 1]
Dictionary:
{brown, dog, fox, jump, lazy, never, over, quick}](https://image.slidesharecdn.com/chatbots-170908095551/85/Using-Machine-Learning-and-Chatbots-to-handle-1st-line-Technical-Support-26-320.jpg)
![Term Frequency - Inverse Document Frequency
𝑡𝑓 𝑡, 𝑑 = 0.5 + 0.5 ∙
𝑓𝑡,𝑑
max
𝑢𝜖𝑑
𝑓𝑢,𝑑
𝑖𝑑𝑓 𝑡, 𝐷 = log
𝐷
{𝑑𝜖𝐷: 𝑡𝜖𝑑}
𝑡𝑓𝑖𝑑𝑓 𝑡, 𝑑, 𝐷 = 𝑡𝑓(𝑡, 𝑑) ∙ 𝑖𝑑𝑓(𝑡, 𝐷)
[0.02, 0.5, ..., 0.001]](https://image.slidesharecdn.com/chatbots-170908095551/85/Using-Machine-Learning-and-Chatbots-to-handle-1st-line-Technical-Support-27-320.jpg)
Uploaded byBarbara Fusinska
Using Machine Learning and Chatbots to handle 1st line Technical Support
The document discusses using machine learning and chatbots for first line technical support. It defines what a chatbot is and provides examples like the Turing test. Chatbots are useful for customer service, technical support via answering frequently asked questions, and automating forms. The document outlines various chatbot platforms and architectures. It also provides an example use case of a chatbot for an IT help desk. The document discusses how natural language processing and classification algorithms can help chatbots understand language. Finally, it demonstrates how tools like LUIS can add intelligence to a technical support chatbot.
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Using Machine Learning and Chatbots to handle 1st line Technical Support
- 1. Using Machine Learningand Chatbots to handle 1st line Technical Support Barbara Fusinska @BasiaFusinska
- 2. About me Programmer Machine Learning DataScientist @BasiaFusinska https://github.com/BasiaFusinska/TechSupportChatBot
- 3. Agenda • What isa chatbot? • Why would you use it? • How to build a chatbot? • What are available tools? • How to apply AI in a chatbot?
- 4.
- 5. Turing Test: Man vsthe Machine
- 6.
- 7.
- 8. Why would you needa machine on the other side?
- 9. Production Chatbots • Customer Service/ Tech Support • FAQ • Scripted scenarios • Forms replacement • Online ordering
- 10.
- 11. Chatbot Platforms Overview • Microsoft’s BotPlatform • Facebook’s Bots for Messenger • Rebot.me • Imperson • ChatScript • Pandorabots
- 12. Microsoft Bot FrameworkArchitecture Receive & Send Messages Process Message System Integration
- 13.
- 14. “Hello” “Hi, something’s wrong withmy computer” “Hello, IT?” “Have you tried to turn it off an on again?” Turning off worked “You are welcome then” “No, no, I will do that. Thanks.” YES “Is it definitely plugged in?” NO “Yes, tried that and nothing happens!” USER CHATBOT
- 15. <<anything>> <<anything>> “Hello, IT?” “Have youtried to turn it off an on again?” Turning off worked “You are welcome then” <<anything containing “no”>> YES “Is it definitely plugged in?” NO <<anything containing “yes”>> USER CHATBOT
- 16. The IT CrowdAnswering Machine Demo
- 17. Scripted solutions • Conversation order •Going off script • Information retrieval • Closed list of answers • Regular expressions
- 18. Chatbots without machinelearning
- 19. Natural Language Processing • Information Extraction •Guessing the context (Intents) • Retrieving data (Named Entity Recognition) • Creative Machines
- 20.
- 21. Classification data Source #Links#Characters ... Fake TopNews 10 2750 … T Twitter 2 120 … F TopNews 235 502 … F Channel X 1530 3024 … T Twitter 24 70 … F StoryLeaks 722 1408 … T Facebook 98 230 … T … … … … ... Features Labels
- 22.
- 23. Bag of words Thequick brown fox jumps over the lazy dog Never jump over the lazy dog quickly Dictionary: {brown, dog, fox, jump, jumps, lazy, never, over, quick, quickly, the}
- 24. Stemming jump, jumps =>jump quick, quickly => quick Dictionary: {brown, dog, fox, jump, lazy, never, over, quick, the}
- 25. Stop words a, an,are, in, is, it, the … Dictionary: {brown, dog, fox, jump, lazy, never, over, quick}
- 26. Frequency vectors The quickbrown fox jumps over the lazy dog [1, 1, 1, 1, 1, 0, 1, 1] Never jump over the lazy dog quickly [0, 1, 0, 1, 1, 1, 1, 1] Dictionary: {brown, dog, fox, jump, lazy, never, over, quick}
- 27. Term Frequency -Inverse Document Frequency 𝑡𝑓 𝑡, 𝑑 = 0.5 + 0.5 ∙ 𝑓𝑡,𝑑 max 𝑢𝜖𝑑 𝑓𝑢,𝑑 𝑖𝑑𝑓 𝑡, 𝐷 = log 𝐷 {𝑑𝜖𝐷: 𝑡𝜖𝑑} 𝑡𝑓𝑖𝑑𝑓 𝑡, 𝑑, 𝐷 = 𝑡𝑓(𝑡, 𝑑) ∙ 𝑖𝑑𝑓(𝑡, 𝐷) [0.02, 0.5, ..., 0.001]
- 28. Applying Artificial Intelligence Receive& Send Messages Process Message System Integration AI (LUIS)
- 29. “Hi, something’s wrong withmy computer” “Have you tried to turn it off an on again?” Already advised “Great, and what is the result?” “No, I will try it now.” YES “Ok, noted. We should schedule the repair” NO “Yes, tried that and nothing happens!” USER CHATBOT Not Working Not Working Try “That’s great. You are welcome then” “I’ve just tried it and it worked!” Worked
- 30. Language Understanding IntelligentService Demo
- 31. Applying Intelligence tothe Chatbot Demo
- 32. LUIS Challenges • Training process •Script vs Intents • Classification errors • Common phrases • Hello/Goodbye • Yes/No
- 33.
- 36.