A conceptual IR chatbot framework with automated keywords-base
This paper proposes a conceptual remodel of Information Retrieval (IR) chatbot framework designed to eliminate the need for large Question-Answer (QA) pair dataset in chatbot's machine learning training and knowledge base development. Within ten proposed framework's components, we describe...
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my.ump.umpir.296662020-11-19T02:01:09Z http://umpir.ump.edu.my/id/eprint/29666/ A conceptual IR chatbot framework with automated keywords-base Abbas Saliimi, Lokman M. A., Ameedeen N., A. Ghani QA76 Computer software This paper proposes a conceptual remodel of Information Retrieval (IR) chatbot framework designed to eliminate the need for large Question-Answer (QA) pair dataset in chatbot's machine learning training and knowledge base development. Within ten proposed framework's components, we describe Ans2Q: a Neural Network model for question type approximation, and HR6: an IR score ranking calculation based on Ans2Q output. Fundamentally, these two components are the variance in which the proposed framework differs from others. Together with process flow explanation, we also provide several related formulas that hopefully can be used to implement this framework. Our general aim with this framework is to provide a tool that can be used to develop close domain chatbot with small knowledge and no readily available QA pair datasets. IOP Publishing 2020 Conference or Workshop Item PeerReviewed pdf en cc_by http://umpir.ump.edu.my/id/eprint/29666/1/27.%20A%20conceptual%20IR%20chatbot%20framework%20with%20automated%20keywords-base.pdf Abbas Saliimi, Lokman and M. A., Ameedeen and N., A. Ghani (2020) A conceptual IR chatbot framework with automated keywords-base. In: IOP Conference Series: Materials Science and Engineering, The 6th International Conference on Software Engineering & Computer Systems, 25-27 September 2019 , Pahang, Malaysia. pp. 1-10., 769 (012020). ISSN 1757-899X |
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QA76 Computer software Abbas Saliimi, Lokman M. A., Ameedeen N., A. Ghani A conceptual IR chatbot framework with automated keywords-base |
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This paper proposes a conceptual remodel of Information Retrieval (IR) chatbot framework designed to eliminate the need for large Question-Answer (QA) pair dataset in chatbot's machine learning training and knowledge base development. Within ten proposed framework's components, we describe Ans2Q: a Neural Network model for question type approximation, and HR6: an IR score ranking calculation based on Ans2Q output. Fundamentally, these two components are the variance in which the proposed framework differs from others. Together with process flow explanation, we also provide several related formulas that hopefully can be used to implement this framework. Our general aim with this framework is to provide a tool that can be used to develop close domain chatbot with small knowledge and no readily available QA pair datasets. |
format |
Conference or Workshop Item |
author |
Abbas Saliimi, Lokman M. A., Ameedeen N., A. Ghani |
author_facet |
Abbas Saliimi, Lokman M. A., Ameedeen N., A. Ghani |
author_sort |
Abbas Saliimi, Lokman |
title |
A conceptual IR chatbot framework with automated keywords-base |
title_short |
A conceptual IR chatbot framework with automated keywords-base |
title_full |
A conceptual IR chatbot framework with automated keywords-base |
title_fullStr |
A conceptual IR chatbot framework with automated keywords-base |
title_full_unstemmed |
A conceptual IR chatbot framework with automated keywords-base |
title_sort |
conceptual ir chatbot framework with automated keywords-base |
publisher |
IOP Publishing |
publishDate |
2020 |
url |
http://umpir.ump.edu.my/id/eprint/29666/1/27.%20A%20conceptual%20IR%20chatbot%20framework%20with%20automated%20keywords-base.pdf http://umpir.ump.edu.my/id/eprint/29666/ |
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1684653575785938944 |