Hi all,
the next tea-talk will be given by Amir Moravej from BotlerAI. The room will be Z209, and the talk starts at 13:45.
Title: AI-Powered Immigration Chatbot
About: Immigration Law refers to government policies which regulate the right of entry, and residency right in each country. Immigration Law absorbs features from other closely related areas of law, including Administrative Law, Constitutional Law, Criminal Law, Tax Law etc. The Immigration Process refers to a set oa steps an individual should take in order to obtain a legal status under Immigration Law. Botler is a Chatbot which automates Immigration Process. From a system perspective, the immigration process can be modeled as a two steps process: Eligibility Assessment, and Application Packaging. Botler models Eligibility Assessment as a rule-based system initiative dialogue using a finite-state dialogue manager which is modeled as a graph of action rules. Application Packaging is a three step process: document authenticity, document relevance, and packaging. Document authenticity is modeled using a CNN for image object recognition. Document relevance is modeled using an OCR for content extraction, and a text processing module to determine content relevancy. Packaging is a two step process, filling out required forms, and creating a map of required documents to the actual documents provided by the user. Botlers’ dataset contains the largest Canadian immigration forum, tens of thousands of conversations in the form of email threads between human legal agents and users, more than five millions of Canadian immigration case eligibility assessment results, and thousands of Immigration Law related documents.
Bio: Amir graduated as a software engineer in 2009, and came to Montreal to continue his graduate studies at Concordia. He finished his Master's degree in Quality Systems Engineering in 2012 and started working in startups ever since. He contributed to the development of several projects, including a content personalization platform for Relevant AI, a QA system for Toti VT, a relevancy engine for nTerop, a data visualization platform for Retinad VR, and the full stack of Squado app. He founded Botler AI in 2016 after he faced several challenges with him immigration process.
Best, --Junyoung
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Hi all,
we have a talk today by Amir Moravej from Botler AI. The room will be Z209 and starts at 13:45.
Best, --Junyoung
On Tue, Apr 4, 2017 at 8:29 PM, Junyoung Chung elecegg@gmail.com wrote:
Hi all,
the next tea-talk will be given by Amir Moravej from BotlerAI. The room will be Z209, and the talk starts at 13:45.
Title: AI-Powered Immigration Chatbot
About: Immigration Law refers to government policies which regulate the right of entry, and residency right in each country. Immigration Law absorbs features from other closely related areas of law, including Administrative Law, Constitutional Law, Criminal Law, Tax Law etc. The Immigration Process refers to a set oa steps an individual should take in order to obtain a legal status under Immigration Law. Botler is a Chatbot which automates Immigration Process. From a system perspective, the immigration process can be modeled as a two steps process: Eligibility Assessment, and Application Packaging. Botler models Eligibility Assessment as a rule-based system initiative dialogue using a finite-state dialogue manager which is modeled as a graph of action rules. Application Packaging is a three step process: document authenticity, document relevance, and packaging. Document authenticity is modeled using a CNN for image object recognition. Document relevance is modeled using an OCR for content extraction, and a text processing module to determine content relevancy. Packaging is a two step process, filling out required forms, and creating a map of required documents to the actual documents provided by the user. Botlers’ dataset contains the largest Canadian immigration forum, tens of thousands of conversations in the form of email threads between human legal agents and users, more than five millions of Canadian immigration case eligibility assessment results, and thousands of Immigration Law related documents.
Bio: Amir graduated as a software engineer in 2009, and came to Montreal to continue his graduate studies at Concordia. He finished his Master's degree in Quality Systems Engineering in 2012 and started working in startups ever since. He contributed to the development of several projects, including a content personalization platform for Relevant AI, a QA system for Toti VT, a relevancy engine for nTerop, a data visualization platform for Retinad VR, and the full stack of Squado app. He founded Botler AI in 2016 after he faced several challenges with him immigration process.
Best, --Junyoung
Hi all,
we have a talk in 10 minutes!
--Junyoung
On Fri, Apr 7, 2017 at 12:50 PM, Junyoung Chung elecegg@gmail.com wrote:
Hi all,
we have a talk today by Amir Moravej from Botler AI. The room will be Z209 and starts at 13:45.
Best, --Junyoung
On Tue, Apr 4, 2017 at 8:29 PM, Junyoung Chung elecegg@gmail.com wrote:
Hi all,
the next tea-talk will be given by Amir Moravej from BotlerAI. The room will be Z209, and the talk starts at 13:45.
Title: AI-Powered Immigration Chatbot
About: Immigration Law refers to government policies which regulate the right of entry, and residency right in each country. Immigration Law absorbs features from other closely related areas of law, including Administrative Law, Constitutional Law, Criminal Law, Tax Law etc. The Immigration Process refers to a set oa steps an individual should take in order to obtain a legal status under Immigration Law. Botler is a Chatbot which automates Immigration Process. From a system perspective, the immigration process can be modeled as a two steps process: Eligibility Assessment, and Application Packaging. Botler models Eligibility Assessment as a rule-based system initiative dialogue using a finite-state dialogue manager which is modeled as a graph of action rules. Application Packaging is a three step process: document authenticity, document relevance, and packaging. Document authenticity is modeled using a CNN for image object recognition. Document relevance is modeled using an OCR for content extraction, and a text processing module to determine content relevancy. Packaging is a two step process, filling out required forms, and creating a map of required documents to the actual documents provided by the user. Botlers’ dataset contains the largest Canadian immigration forum, tens of thousands of conversations in the form of email threads between human legal agents and users, more than five millions of Canadian immigration case eligibility assessment results, and thousands of Immigration Law related documents.
Bio: Amir graduated as a software engineer in 2009, and came to Montreal to continue his graduate studies at Concordia. He finished his Master's degree in Quality Systems Engineering in 2012 and started working in startups ever since. He contributed to the development of several projects, including a content personalization platform for Relevant AI, a QA system for Toti VT, a relevancy engine for nTerop, a data visualization platform for Retinad VR, and the full stack of Squado app. He founded Botler AI in 2016 after he faced several challenges with him immigration process.
Best, --Junyoung
lisa_seminaires@iro.umontreal.ca
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Junyoung Chung