Enhancing NLP Systems for Improved and Intelligent Multi Intent Recognition and Handling of Alarming situations

Authors

  • Chithra Apoorva D. A. Research Scholar GITAM School of Technology, Banglore, India
  • Brahmananda S. H. Professor, Computer Science Engineering. GITAM School of Technology, Banglore, India

Keywords:

complexity, NLP(Natural Language Processing), algorithm, asynchronous

Abstract

NLP (Natural Language Processing) is one of the blistering research topics of the day. Many of the complex and multi feature classification problems are solved using the machine learning neural net model. In this research work, the multi-intent classification has been concentrated, Where the algorithm identifies the entity mapping in a right direction. The multiple entities in the same sentence is mapped using the proposed novel intelligent multi intent recognition system. The system proposed is able to capture the alarming situation that can be handled, or which are ignored by the existing machine learning models. The results demonstrate how the model reduces the time consumption and the complexity which are developed by invoking the asynchronous method in several parallel processing task.

Downloads

Download data is not yet available.

References

Brown, P., & Manning, C. D. (2019). A Survey of Natural Language Processing Techniques for Chatbot Design. Computational Linguistics, 45(3), 537-566.

Vaswani, A., et al. (2017). Attention Is All You Need. In Proceedings of the 31st Conference on Neural Information Processing Systems (NeurIPS 2017) (pp. 6000-6010).

Kim, Y. (2014). Convolutional Neural Networks for Sentence Classification. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP 2014) (pp. 1746-1751).

Collobert, R., et al. (2011). Natural Language Processing (Almost) from Scratch. Journal of Machine Learning Research, 12, 2493-2537.

Pennington, J., Socher, R., & Manning, C. D. (2014). GloVe: Global Vectors for Word Representation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP 2014) (pp. 1532-1543).

Ruder, S. (2017). An Overview of Multi-Task Learning in Deep Neural Networks. arXiv preprint arXiv:1706.05098.

Chen, X., Liu, Z., & Sun, M. (2017). A Survey of Weakly Supervised Learning for Named Entity Recognition and Classification. ACM Transactions on Knowledge Discovery from Data (TKDD), 11(3), 1-28.

Devlin, J., Chang, M. W., & Lee, K. (2018). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv preprint arXiv:1810.04805.

Sutskever, I., Vinyals, O., & Le, Q. V. (2014). Sequence to Sequence Learning with Neural Networks. In Advances in Neural Information Processing Systems (NeurIPS 2014) (pp. 3104-3112).

Chen, Y., & Bansal, M. (2018). Fast Abstractive Summarization with Reinforce-Selected Sentence Rewriting. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP 2018) (pp. 281-286).

Liu, P. J., et al. (2019). Multimodal Transformer for Unaligned Multimodal Language Sequences. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP 2019) (pp. 61-72).

Xu, K., et al. (2016). Show, Attend and Tell: Neural Image Caption Generation with Visual Attention. In International Conference on Machine Learning (ICML 2015) (pp. 2048-2057).

Anil Kumar C, Lavanya Vaishnavi D A, “Renesas Based Smart ROBOT using Embedded System”, International Journal of Research in Science, Commerce, Arts, Management and Technology, Volume 2, Issue 1, February 2021, ISSN (Online) 2582-8045

Rajpurkar, P., et al. (2016). SQuAD: 100,000+ Questions for Machine Comprehension of Text. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing (EMNLP 2016) (pp. 2383-2392).

Henderson, P., et al. (2019). Training Deeper Neural Machine Translation Models with Transparent Attention. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019) (pp. 4944-4958).

Wang, A., et al. (2019). StructBERT: Incorporating Language Structures into Pre-training for Deep Language Understanding. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019) (pp. 4250-4260).arXiv:1904.08524 [cs.IR], https://doi.org/10.48550/arXiv.1904.08524

L. Qiu, Y. Chen, H. Jia and Z. Zhang, "Query Intent Recognition Based on Multi-Class Features," in IEEE Access, vol. 6, pp. 52195-52204, 2018, doi: 10.1109/ACCESS.2018.2869585.

Xiaodong Zhang, Houfeng Wang, “A joint model of intent determination and slot filling for spoken language understanding” IJCAI'16

Anil kumar et al., "Algorithm for Recognition of Movement of Objects in a Video Surveillance System Using a Neural Network", Hindawi Journal of Engineering, vol. 2022, Article ID 8216221,2022. https://doi.org/10.1155/2022/8216221

Libo Qin, Xiao Xu, Wanxiang Che, and Ting Liu. 2020. AGIF: An Adaptive Graph-Interactive Framework for Joint Multiple Intent Detection and Slot Filling. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 1807–1816, Online. Association for Computational Linguistics.

Devlin, J., Chang, M. W., & Lee, K. (2018). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv preprint arXiv:1810.04805.

Sutskever, I., Vinyals, O., & Le, Q. V. (2014). Sequence to Sequence Learning with Neural Networks. In Advances in Neural Information Processing Systems (NeurIPS 2014) (pp. 3104-3112).

Ana Rodriguez, Kristinsdóttir María, Pekka Koskinen, Pieter van der Meer, Thomas Müller. Robust Decision Making through Machine Learning in Decision Science. Kuwait Journal of Machine Learning, 2(4). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/215

Ramesh, P. V. ., Hrishikesh, J. T. ., & Patil, M. S. . (2023). Infant’s MRI Brain Tissue Segmentation using Integrated CNN Feature Extractor and Random Forest. International Journal on Recent and Innovation Trends in Computing and Communication, 11(1s), 71–79. https://doi.org/10.17762/ijritcc.v11i1s.6002

Dhabliya, D., Ugli, I.S.M., Murali, M.J., Abbas, A.H.R., Gulbahor, U. Computer Vision: Advances in Image and Video Analysis (2023) E3S Web of Conferences, 399, art. no. 04045, .

Downloads

Published

10.11.2023

How to Cite

D. A., C. A. ., & S. H., B. . (2023). Enhancing NLP Systems for Improved and Intelligent Multi Intent Recognition and Handling of Alarming situations. International Journal of Intelligent Systems and Applications in Engineering, 12(4s), 228–234. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3786

Issue

Vol. 12 No. 4s (2024)

Section

Research Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.

IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.