Tom Amon Aaron Campbell Stakeholder: Marcello …...WHY DREXEL CHATBOT Why API • Expandable to...
Transcript of Tom Amon Aaron Campbell Stakeholder: Marcello …...WHY DREXEL CHATBOT Why API • Expandable to...
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Members:
Tom Amon
Aaron Campbell
Daniel Fitzick
Shishir Kharel
Hoa Vu
Nanxi Zhang
Stakeholder:
Marcello Balduccini
5/24/2017 1
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• Introduction & Features
• Overview of Algorithm
• System Architecture & Design
• Management
• Demo
• Limitations & Technical Challenges
DREXEL CHATBOT
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INTRODUCTION & FEATURES
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WHAT IS DREXELCHATBOT
• Question answering system for Drexel
• RESTful API
• Demo applications (Web, Mobile and SMS)
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WHY DREXEL CHATBOT
Compared to Google:
• More efficient
• More user friendly
• Gets results faster
Compared to Google Assistant/Siri:
• Optimized for Drexel
• More information about Drexel
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WHY DREXEL CHATBOT
Why API
• Expandable to other applications
• Friendly to other developers
• Research project
• Focus on the process
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FEATURES
• Input and output in natural language
• API
• Input by URL parameters
• Output formatted in JSON
• Concurrent users
• Demo applications
• Simple interfaces
• Enter and send questions; get responses
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SUPPORTED QUESTIONS
Faculty
• Website
• Title
• Office
• Phone number
• Picture
• Department
• Education
• Publications
• Research interests
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Buildings
• Address
• Schedule
• Picture
• Website
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OVERVIEW OF ALGORITHM
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INSPIRATION
Google’s “A neural conversational model” (2015)
• I am seeing an error related to VPN
• What is the error message that you are getting when connecting to VPN using NetworkConnect ?
• Connection refused
• May I know the version of NetworkConnect you are using?
• Wheres Marcello Balduccini office?
o Marcello Balduccini office is Rush 233C.
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Where is Vokolos’ office?
Where is (Faculty)’s office?
(Faculty)’s office is in (Room). [query]
Vokolos’ office is in University Crossings 131.
Generalize
Classify
Populate
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Where is Vokolos’ office?
Where is (faculty)’s office?
(faculty)’s office is in (room). [query]
Vokolos’ office is in University Crossings 131.
Generalize
Classify
Populate
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Where is Vokolos’ office?
Where is (faculty)’s office?
(faculty)’s office is in (room). [query]
Vokolos’ office is in University Crossings 131.
Generalize
Classify
Populate
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Where is Vokolos’ office?
Where is (faculty)’s office?
(faculty)’s office is in (room). [query]
Vokolos’ office is in University Crossings 131.
Generalize
Classify
Populate
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ALGORITHM DESIGN RATIONALE
• Less training data
• Modular
• Adaptable
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SYSTEM ARCHITECTURE & DESIGN
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MAIN LANGUAGES
• Frontend
• Java
• Java Script
• HTML & CSS
• API: Java
• Backend: Python
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API• Basic Model-View-Controller
• Calls backend on each GET request
• Each requests gets separate process
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GENERIC QUESTION CONSTRUCTION
• “Generalize” step
• Natural Language Processing
• Identify keywords
• Part of speech tagging
• Database lookup
• Replace keywords with generic form
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GENERIC ANSWER CONSTRUCTION
• “Classify” step
• Neural network
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GENERIC ANSWER CONSTRUCTION
• 18 answer categories
• ~200 training questions
• ~7000 training question after adding noise (misspelling)
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GENERIC ANSWER POPULATION
• Simplest component
• Query the database
• Replace generic form
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DATABASE• RDF Database
• No tables or keys
• Extensibility
• One-to-many
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INFORMATION EXTRACTION
• Populates the database
• 20 scripts
• Creates TTL(turtle) files
<#Denise-Agosto>
cb:name "Denise Agosto" ;
cb:room "Rush 214B" ;
cb:email "[email protected]" ;
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ERROR HANDLER
• Allows failing more gracefully
• 2 types of errors
• “I don’t understand the question”
• “Sorry I don't have an answer for that. But I found this website: http:...”
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MANAGEMENT
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COMMUNICATION
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PLANNING
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REPOSITORY & BUG TRACKING
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TESTING• Unit testing
• System testing
• User testing
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DEMO
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LIMITATIONS & TECHNICAL CHALLENGES
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LIMITATIONS
Input question
• No compound
• Context-unaware
• Keyword-dependent
NLTK
• Misidentification
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TECHNICAL CHALLENGES
• Database speed
• Neural network speed
• Lack of data
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CONCLUSION
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QUESTIONS
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T HA N K YO U
Aaron, Daniel, Hoa, Nanxi, Shishir, Tom, and Bun
5/24/2017 38