Cognition-based document matching within the chatbot modeling framework

Jatmika, Sunu ORCID: https://orcid.org/0000-0002-6833-4555, Patmanthara, Syaad ORCID: https://orcid.org/0000-0002-3709-8764, Wibawa, Aji Prasetya and Kurniawan, Fachrul ORCID: https://orcid.org/0000-0002-3709-8764 (2024) Cognition-based document matching within the chatbot modeling framework. Journal of Applied Data Sciences, 5 (2). Indonesia. ISSN 2723-6471

Text 21123.pdf Download (367kB)

Abstract

The aim of the study is to examine cognitive methods for document matching in a chatbot modeling framework by utilizing Euclidean Distance,
Cosine Similarity, and BERT methodologies. Five primary indications are used to carry out evaluation in testing: document matching accuracy,
document matching execution time, document search efficiency, consistency of document matching results, and the quality of the document
representation in the matrix. Document matching accuracy is evaluated by precision; document matching execution time is measured from the
beginning to the end of the document matching process; document search efficiency is measured through evaluation of execution time and
matching accuracy; the consistency of document matching results is assessed by comparing method results when tested against the same or
similar queries and the quality of document representation is assessed based on the method's ability to represent documents in a matrix or vector.
The test findings offer a comprehensive understanding of how well the three approaches operate and exhibit their capacity to address the unique
requirements of chatbot users. These results may contribute to the advancement of language technology applications, making it possible for
chatbots to deliver pertinent information more rapidly and precisely. There are 1,755 labeled question samples in the dataset, which were split up
into two sets: 60% for training (1,053 pieces), and 40% for testing (702 samples) to evaluate the model's performance. The test results show the
accuracy of the three methods based on five measured evaluation indications, namely Euclidean Distance 0,45%, Cosine similarity 0,59%, and
BERT 0,91%. By comprehending the benefits and drawbacks of each approach, this research strengthens contributions to the growth of chatbot
systems to better serve user demands and opens the door for the creation of more complex human-machine interaction solutions.

Item Type:	Journal Article
Keywords:	Document Matching; Chatbot Models; Evaluation Method; Method Performance; AI Chatbot
Subjects:	08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080199 Artificial Intelligence and Image Processing not elsewhere classified
Divisions:	Faculty of Technology > Department of Informatics Engineering
Depositing User:	Fachrul Kurniawan
Date Deposited:	31 Oct 2024 11:08

Downloads

Origin of downloads

Actions (login required)

View Item