TY - GEN
T1 - Analysis of Traditional and Deep Learning Architectures in NLP
T2 - 6th International Conference on Recent Trends in Advance Computing, ICRTAC 2023
AU - Subbulakshmi, T.
AU - Narayan, Prathiba Lakshmi
AU - Suthraye Gokulnath, Shreejith
AU - Suganya, R.
AU - Subramanian, Girish H.
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - In the ever-evolving landscape of Natural Language Processing (NLP), the development of novel architectures and optimization techniques has been instrumental in advancing the field. This survey paper presents a comprehensive exploration of traditional and deep learning architectures employed in NLP, while also delving into the optimization strategies that enhance their performance. With a primary focus on surveying existing literature, the aim is to provide a holistic view of the landscape of NLP architectures and their associated optimization techniques. Additionally, a novel architecture is introduced that promises to contribute to the ongoing progress in NLP. This architecture, detailed in our full paper, combines the best practices and innovations from traditional and deep learning models to tackle NLP tasks effectively. Through this work, the aim is to contribute to the collective knowledge in NLP and facilitate future advancements in the field.
AB - In the ever-evolving landscape of Natural Language Processing (NLP), the development of novel architectures and optimization techniques has been instrumental in advancing the field. This survey paper presents a comprehensive exploration of traditional and deep learning architectures employed in NLP, while also delving into the optimization strategies that enhance their performance. With a primary focus on surveying existing literature, the aim is to provide a holistic view of the landscape of NLP architectures and their associated optimization techniques. Additionally, a novel architecture is introduced that promises to contribute to the ongoing progress in NLP. This architecture, detailed in our full paper, combines the best practices and innovations from traditional and deep learning models to tackle NLP tasks effectively. Through this work, the aim is to contribute to the collective knowledge in NLP and facilitate future advancements in the field.
UR - http://www.scopus.com/inward/record.url?scp=85190639803&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85190639803&partnerID=8YFLogxK
U2 - 10.1109/ICRTAC59277.2023.10480825
DO - 10.1109/ICRTAC59277.2023.10480825
M3 - Conference contribution
AN - SCOPUS:85190639803
T3 - Proceedings of the 2023 6th International Conference on Recent Trends in Advance Computing, ICRTAC 2023
SP - 760
EP - 765
BT - Proceedings of the 2023 6th International Conference on Recent Trends in Advance Computing, ICRTAC 2023
A2 - Ganesan, R.
A2 - Harikrishnan, K.
A2 - Parvathi, R.
A2 - Geetha, S.
A2 - Thomas Abraham, J.V.
A2 - Vedhapriyavadhana, R.
A2 - Murugesan, Rajkumar
A2 - Kalaipriyan, T.
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 14 December 2023 through 15 December 2023
ER -