Progressive Guidance Categorization Using Transformer-Based Deep Neural Network Architecture

Tanjim Taharat Aurpa; Md Shoaib Ahmed; Rifat Sadik; Sabbir Anwar; Md Abdul Mazid Adnan; Md Musfique Anwar

doi:10.1007/978-3-030-96305-7_32

Progressive Guidance Categorization Using Transformer-Based Deep Neural Network Architecture

Tanjim Taharat Aurpa^*, Md Shoaib Ahmed, Rifat Sadik, Sabbir Anwar, Md Abdul Mazid Adnan, Md Musfique Anwar

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

7 Scopus citations

Abstract

In the era of advanced technology, the internet is one of the most indispensable necessities in our daily liveliness. Usually, people search for various things on a search engine, from daily needs to dream utilities. From the ample amount of searching styles, the prefix with “How To" is one of the exploited prominent styles. According to this search style with the mentioned prefix, people mainly find out the guidelines or instructions for solutions. However, there is no work has been conducted on guidelines or instructions classification in the past. In this research work, we identify the category of guidelines or instructions from the summary of “How To” articles. We employ some deep neural architectures, such as LSTM (Long Short-Term Memory), RNN (Recurrent Neural Network), and BERT (Bidirectional Encoder Representations from Transformers) to classify guidelines precisely and swiftly. We have conveyed this work with a novel dataset comprising 11,121 observations from the WikiHow. In this classification process, we have manifested accuracy, precision, recall, and f1-score to appraise our proposed architectures. The outcomes have brought a wit of our applied BERT architecture that achieves notably with 87% accuracy.

Original language	English
Title of host publication	Hybrid Intelligent Systems - 21st International Conference on Hybrid Intelligent Systems, HIS 2021
Editors	Ajith Abraham, Patrick Siarry, Vincenzo Piuri, Niketa Gandhi, Gabriella Casalino, Oscar Castillo, Patrick Hung
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	344-353
Number of pages	10
ISBN (Print)	9783030963040
DOIs	https://doi.org/10.1007/978-3-030-96305-7_32
State	Published - 2022
Externally published	Yes
Event	21st International Conference on Hybrid Intelligent Systems, HIS 2021 and 17th International Conference on Information Assurance and Security, IAS 2021 - Virtual, Online Duration: 14 Dec 2021 → 16 Dec 2021

Publication series

Name	Lecture Notes in Networks and Systems
Volume	420 LNNS
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	21st International Conference on Hybrid Intelligent Systems, HIS 2021 and 17th International Conference on Information Assurance and Security, IAS 2021
City	Virtual, Online
Period	14/12/21 → 16/12/21

Bibliographical note

Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Keywords

BERT
Guidelines and Instruction
Guidelines classify
Instruction classify
Transformer-based learning
WikiHow

ASJC Scopus subject areas

Control and Systems Engineering
Signal Processing
Computer Networks and Communications

Access to Document

10.1007/978-3-030-96305-7_32

Cite this

Aurpa, T. T., Ahmed, M. S., Sadik, R., Anwar, S., Adnan, M. A. M., & Anwar, M. M. (2022). Progressive Guidance Categorization Using Transformer-Based Deep Neural Network Architecture. In A. Abraham, P. Siarry, V. Piuri, N. Gandhi, G. Casalino, O. Castillo, & P. Hung (Eds.), Hybrid Intelligent Systems - 21st International Conference on Hybrid Intelligent Systems, HIS 2021 (pp. 344-353). (Lecture Notes in Networks and Systems; Vol. 420 LNNS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-96305-7_32

Aurpa, Tanjim Taharat ; Ahmed, Md Shoaib ; Sadik, Rifat et al. / Progressive Guidance Categorization Using Transformer-Based Deep Neural Network Architecture. Hybrid Intelligent Systems - 21st International Conference on Hybrid Intelligent Systems, HIS 2021. editor / Ajith Abraham ; Patrick Siarry ; Vincenzo Piuri ; Niketa Gandhi ; Gabriella Casalino ; Oscar Castillo ; Patrick Hung. Springer Science and Business Media Deutschland GmbH, 2022. pp. 344-353 (Lecture Notes in Networks and Systems).

@inproceedings{2a6b38dce6194673a83e5d6f542b551e,

title = "Progressive Guidance Categorization Using Transformer-Based Deep Neural Network Architecture",

abstract = "In the era of advanced technology, the internet is one of the most indispensable necessities in our daily liveliness. Usually, people search for various things on a search engine, from daily needs to dream utilities. From the ample amount of searching styles, the prefix with “How To{"} is one of the exploited prominent styles. According to this search style with the mentioned prefix, people mainly find out the guidelines or instructions for solutions. However, there is no work has been conducted on guidelines or instructions classification in the past. In this research work, we identify the category of guidelines or instructions from the summary of “How To” articles. We employ some deep neural architectures, such as LSTM (Long Short-Term Memory), RNN (Recurrent Neural Network), and BERT (Bidirectional Encoder Representations from Transformers) to classify guidelines precisely and swiftly. We have conveyed this work with a novel dataset comprising 11,121 observations from the WikiHow. In this classification process, we have manifested accuracy, precision, recall, and f1-score to appraise our proposed architectures. The outcomes have brought a wit of our applied BERT architecture that achieves notably with 87\% accuracy.",

keywords = "BERT, Guidelines and Instruction, Guidelines classify, Instruction classify, Transformer-based learning, WikiHow",

author = "Aurpa, \{Tanjim Taharat\} and Ahmed, \{Md Shoaib\} and Rifat Sadik and Sabbir Anwar and Adnan, \{Md Abdul Mazid\} and Anwar, \{Md Musfique\}",

note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 21st International Conference on Hybrid Intelligent Systems, HIS 2021 and 17th International Conference on Information Assurance and Security, IAS 2021 ; Conference date: 14-12-2021 Through 16-12-2021",

year = "2022",

doi = "10.1007/978-3-030-96305-7\_32",

language = "English",

isbn = "9783030963040",

series = "Lecture Notes in Networks and Systems",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "344--353",

editor = "Ajith Abraham and Patrick Siarry and Vincenzo Piuri and Niketa Gandhi and Gabriella Casalino and Oscar Castillo and Patrick Hung",

booktitle = "Hybrid Intelligent Systems - 21st International Conference on Hybrid Intelligent Systems, HIS 2021",

address = "Germany",

}

Aurpa, TT, Ahmed, MS, Sadik, R, Anwar, S, Adnan, MAM & Anwar, MM 2022, Progressive Guidance Categorization Using Transformer-Based Deep Neural Network Architecture. in A Abraham, P Siarry, V Piuri, N Gandhi, G Casalino, O Castillo & P Hung (eds), Hybrid Intelligent Systems - 21st International Conference on Hybrid Intelligent Systems, HIS 2021. Lecture Notes in Networks and Systems, vol. 420 LNNS, Springer Science and Business Media Deutschland GmbH, pp. 344-353, 21st International Conference on Hybrid Intelligent Systems, HIS 2021 and 17th International Conference on Information Assurance and Security, IAS 2021, Virtual, Online, 14/12/21. https://doi.org/10.1007/978-3-030-96305-7_32

Progressive Guidance Categorization Using Transformer-Based Deep Neural Network Architecture. / Aurpa, Tanjim Taharat; Ahmed, Md Shoaib; Sadik, Rifat et al.
Hybrid Intelligent Systems - 21st International Conference on Hybrid Intelligent Systems, HIS 2021. ed. / Ajith Abraham; Patrick Siarry; Vincenzo Piuri; Niketa Gandhi; Gabriella Casalino; Oscar Castillo; Patrick Hung. Springer Science and Business Media Deutschland GmbH, 2022. p. 344-353 (Lecture Notes in Networks and Systems; Vol. 420 LNNS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Progressive Guidance Categorization Using Transformer-Based Deep Neural Network Architecture

AU - Aurpa, Tanjim Taharat

AU - Ahmed, Md Shoaib

AU - Sadik, Rifat

AU - Anwar, Sabbir

AU - Adnan, Md Abdul Mazid

AU - Anwar, Md Musfique

PY - 2022

Y1 - 2022

N2 - In the era of advanced technology, the internet is one of the most indispensable necessities in our daily liveliness. Usually, people search for various things on a search engine, from daily needs to dream utilities. From the ample amount of searching styles, the prefix with “How To" is one of the exploited prominent styles. According to this search style with the mentioned prefix, people mainly find out the guidelines or instructions for solutions. However, there is no work has been conducted on guidelines or instructions classification in the past. In this research work, we identify the category of guidelines or instructions from the summary of “How To” articles. We employ some deep neural architectures, such as LSTM (Long Short-Term Memory), RNN (Recurrent Neural Network), and BERT (Bidirectional Encoder Representations from Transformers) to classify guidelines precisely and swiftly. We have conveyed this work with a novel dataset comprising 11,121 observations from the WikiHow. In this classification process, we have manifested accuracy, precision, recall, and f1-score to appraise our proposed architectures. The outcomes have brought a wit of our applied BERT architecture that achieves notably with 87% accuracy.

AB - In the era of advanced technology, the internet is one of the most indispensable necessities in our daily liveliness. Usually, people search for various things on a search engine, from daily needs to dream utilities. From the ample amount of searching styles, the prefix with “How To" is one of the exploited prominent styles. According to this search style with the mentioned prefix, people mainly find out the guidelines or instructions for solutions. However, there is no work has been conducted on guidelines or instructions classification in the past. In this research work, we identify the category of guidelines or instructions from the summary of “How To” articles. We employ some deep neural architectures, such as LSTM (Long Short-Term Memory), RNN (Recurrent Neural Network), and BERT (Bidirectional Encoder Representations from Transformers) to classify guidelines precisely and swiftly. We have conveyed this work with a novel dataset comprising 11,121 observations from the WikiHow. In this classification process, we have manifested accuracy, precision, recall, and f1-score to appraise our proposed architectures. The outcomes have brought a wit of our applied BERT architecture that achieves notably with 87% accuracy.

KW - BERT

KW - Guidelines and Instruction

KW - Guidelines classify

KW - Instruction classify

KW - Transformer-based learning

KW - WikiHow

UR - http://www.scopus.com/inward/record.url?scp=85126338914&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-96305-7_32

DO - 10.1007/978-3-030-96305-7_32

M3 - Conference contribution

AN - SCOPUS:85126338914

SN - 9783030963040

T3 - Lecture Notes in Networks and Systems

SP - 344

EP - 353

BT - Hybrid Intelligent Systems - 21st International Conference on Hybrid Intelligent Systems, HIS 2021

A2 - Abraham, Ajith

A2 - Siarry, Patrick

A2 - Piuri, Vincenzo

A2 - Gandhi, Niketa

A2 - Casalino, Gabriella

A2 - Castillo, Oscar

A2 - Hung, Patrick

PB - Springer Science and Business Media Deutschland GmbH

T2 - 21st International Conference on Hybrid Intelligent Systems, HIS 2021 and 17th International Conference on Information Assurance and Security, IAS 2021

Y2 - 14 December 2021 through 16 December 2021

ER -

Aurpa TT, Ahmed MS, Sadik R, Anwar S, Adnan MAM, Anwar MM. Progressive Guidance Categorization Using Transformer-Based Deep Neural Network Architecture. In Abraham A, Siarry P, Piuri V, Gandhi N, Casalino G, Castillo O, Hung P, editors, Hybrid Intelligent Systems - 21st International Conference on Hybrid Intelligent Systems, HIS 2021. Springer Science and Business Media Deutschland GmbH. 2022. p. 344-353. (Lecture Notes in Networks and Systems). doi: 10.1007/978-3-030-96305-7_32

Progressive Guidance Categorization Using Transformer-Based Deep Neural Network Architecture

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

Fingerprint

Cite this