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Salud, Ciencia y Tecnología Metaverse Basic and Applied Research Data & Metadata Interdisciplinary Rehabilitation Salud, Ciencia y Tecnología - Serie de Conferencias Community and Interculturality in Dialogue Seminars in Medical Writing and Education Health Leadership and Quality of Life Gamification and Augmented Reality Southern perspective / Perspectiva austral
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Original
Published: 2024-05-12
DOI: https://doi.org/10.56294/sctconf2024758

eHyPRETo: Enhanced Hybrid Pre-Trained and Transfer Learning-based Contextual Relation Classification Model

By
Jeyakodi G ,
Jeyakodi G

Department of Computer Science, Pondicherry University, Puducherry, India

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Sarojini B ,
Sarojini B

Department of Computer Science, Avinashilingam Institute for Home Science and Higher Education For Women, Coimbatore, India

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Shanthi Bala P ,
Shanthi Bala P

Department of Computer Science, Pondicherry University, Puducherry, India

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Abstract

Introduction: Relation classification (RC) plays a crucial role in enhancing the
understanding of intricate relationships, as it helps with many NLP (Natural Language
Processing) applications. To identify contextual subtleties in different domains, one might
make use of pre-trained models.
Methods: To achieve successful relation classification, a recommended model called
eHyPRETo, which is a hybrid pre-trained model, has to be used. The system comprises
several components, including ELECTRA, RoBERTa, and Bi-LSTM. The integration of pre-
trained models enabled the utilisation of Transfer Learning (TL) to acquire contextual
information and complex patterns. Therefore, the amalgamation of pre-trained models has
great importance. The major purpose of this related classification is to effectively handle
irregular input and improve the overall efficiency of pre-trained models. The analysis of
eHyPRETo involves the use of a carefully annotated biological dataset focused on Indian
Mosquito Vector Biocontrol Agents.
Results: The eHyPRETo model has remarkable stability and effectiveness in categorising, as
evidenced by its continuously high accuracy of 98.73% achieved during training and
evaluation throughout several epochs. The eHyPRETo model's domain applicability was
assessed. The obtained p-value of 0.06 indicates that the model is successful and adaptable
across many domains.
Conclusion: The suggested hybrid technique has great promise for practical applications
such as medical diagnosis, financial fraud detection, climate change analysis, targeted
marketing campaigns, and self-driving automobile navigation, among others. The eHyPRETo
model has been developed in response to the challenges in RC, representing a significant
advancement in the fields of linguistics and artificial intelligence.

Keywords:

Pre-trained Model,
RC (Relation Classification),
Contextual,
ELECTRA,
RoBERTa,
Domain Adaptable,
eHyPRETo,

How to Cite

1.
G J, B S, Bala P S. eHyPRETo: Enhanced Hybrid Pre-Trained and Transfer Learning-based Contextual Relation Classification Model. Salud, Ciencia y Tecnología - Serie de Conferencias [Internet]. 2024 May 12 [cited 2024 Jun. 8];3:758. Available from: https://conferencias.saludcyt.ar/index.php/sctconf/article/view/758

Copyright Notice

The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.

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Vol. 3 (2024)
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