Human-Centric Intelligent Systems

In Press, Corrected Proof, Available Online: 20 November 2021

Pretrained Natural Language Processing Model for Intent Recognition (BERT-IR)

Authors
Vasima Khan1, *, Tariq Azfar Meenai2
1Department of Computer Science & Engineering, Sagar Institute of Science & Technology (SISTec), Bhopal, Madhya Pradesh, India
2Department of Electronics & Communication, Smith Infotech Pvt. Ltd.
*Corresponding author. Email: drvasimakhan88@gmail.com
Corresponding Author
Vasima Khan
Received 15 July 2021, Accepted 25 October 2021, Available Online 20 November 2021.
DOI
https://doi.org/10.2991/hcis.k.211109.001How to use a DOI?
Keywords
Intent recognition; intent detection; natural language processing; BERT; deep learning; deep neural network
Abstract

Intent Recognition (IR) is considered a key area in Natural Language Processing (NLP). It has crucial usage in various applications. One is the Search Engine-Interpreting the context of text searched by the user improves the response time and helps the search engines give appropriate outputs. Another can be Social Media Analytics-Analysing profiles of users on different social media platforms has become a necessity in today’s applications like recommendation systems in the online world, digital marketing, and a lot more. Many researchers are using different techniques for achieving intent recognition but getting high accuracy in intent recognition is crucial. In this work, named BERT-IR, a pre-trained Natural Language Processing model called as BERT model, along with few add-ons, is applied for the task of Intent Recognition. We have achieved an accuracy of 97.67% on a widely used dataset which shows the capability and efficiency of our work. For comparison purposes, we have applied primarily used Machine Learning techniques, namely Naive Bayes, Logistic Regression, Decision Tree, Random Forest, and Gradient Boost as well as Deep Learning Techniques used for intent recognition like Recurrent Neural Network, Long Short Term Memory Network, and Bidirectional Long Short Term Memory Network on the same dataset and evaluated the accuracy. It is found out that BERT-IR’s accuracy is far better than that of the other models implemented.

Copyright
© 2021 The Authors. Publishing services by Atlantis Press International B.V.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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Journal
Human-Centric Intelligent Systems
Publication Date
2021/11/20
ISSN (Online)
2667-1336
DOI
https://doi.org/10.2991/hcis.k.211109.001How to use a DOI?
Copyright
© 2021 The Authors. Publishing services by Atlantis Press International B.V.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - JOUR
AU  - Vasima Khan
AU  - Tariq Azfar Meenai
PY  - 2021
DA  - 2021/11/20
TI  - Pretrained Natural Language Processing Model for Intent Recognition (BERT-IR)
JO  - Human-Centric Intelligent Systems
SN  - 2667-1336
UR  - https://doi.org/10.2991/hcis.k.211109.001
DO  - https://doi.org/10.2991/hcis.k.211109.001
ID  - Khan2021
ER  -