Advancements in natural language processing: Implications, challenges, and future directions
This research delves into the latest advancements in Natural Language Processing (NLP) and their broader implications, challenges, and future directions. With the ever-increasing volume of text data generated daily from diverse sources, extracting relevant and valuable information is becoming more c...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Telematics and Informatics Reports |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772503024000598 |
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| _version_ | 1846119301043453952 |
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| author | Supriyono Aji Prasetya Wibawa Suyono Fachrul Kurniawan |
| author_facet | Supriyono Aji Prasetya Wibawa Suyono Fachrul Kurniawan |
| author_sort | Supriyono |
| collection | DOAJ |
| description | This research delves into the latest advancements in Natural Language Processing (NLP) and their broader implications, challenges, and future directions. With the ever-increasing volume of text data generated daily from diverse sources, extracting relevant and valuable information is becoming more complex. Conventional manual techniques for handling and examining written information are laborious and susceptible to mistakes, underscoring the necessity for effective automated alternatives. The advancements in Natural Language Processing (NLP), namely in transformer-based models and deep learning techniques, have demonstrated considerable potential in improving the precision and consistency of various NLP applications. This work presents a novel approach that combines systematic review methods with sophisticated NLP approaches to enhance the overall efficiency of NLP systems. The proposed strategy guarantees an organized and clear literature review process, resulting in more informative and contextually relevant results. The report examines NLP's implications, problems, and opportunities, providing significant insights that are anticipated to propel improvements in NLP technology and its application in many industries. |
| format | Article |
| id | doaj-art-077f591c9ae847e79ac05dbf95b61e94 |
| institution | Kabale University |
| issn | 2772-5030 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Telematics and Informatics Reports |
| spelling | doaj-art-077f591c9ae847e79ac05dbf95b61e942024-12-17T05:02:16ZengElsevierTelematics and Informatics Reports2772-50302024-12-0116100173Advancements in natural language processing: Implications, challenges, and future directions Supriyono0Aji Prasetya Wibawa1 Suyono2Fachrul Kurniawan3Department of Electrical Engineering and Informatics, Faculty of Engineering, Universitas Negeri Malang, Jl. Semarang no. 5, Malang 65145, Indonesia; Informatics Engineering, Faculty of Science and Technology, Universitas Islam Negeri Maulana Malik Ibrahim Malang, IndonesiaDepartment of Electrical Engineering and Informatics, Faculty of Engineering, Universitas Negeri Malang, Jl. Semarang no. 5, Malang 65145, Indonesia; Corresponding author.Department of Indonesian Literature, Faculty of Letters, Universitas Negeri Malang, Jl. Semarang no. 5, Malang 65145, IndonesiaInformatics Engineering, Faculty of Science and Technology, Universitas Islam Negeri Maulana Malik Ibrahim Malang, IndonesiaThis research delves into the latest advancements in Natural Language Processing (NLP) and their broader implications, challenges, and future directions. With the ever-increasing volume of text data generated daily from diverse sources, extracting relevant and valuable information is becoming more complex. Conventional manual techniques for handling and examining written information are laborious and susceptible to mistakes, underscoring the necessity for effective automated alternatives. The advancements in Natural Language Processing (NLP), namely in transformer-based models and deep learning techniques, have demonstrated considerable potential in improving the precision and consistency of various NLP applications. This work presents a novel approach that combines systematic review methods with sophisticated NLP approaches to enhance the overall efficiency of NLP systems. The proposed strategy guarantees an organized and clear literature review process, resulting in more informative and contextually relevant results. The report examines NLP's implications, problems, and opportunities, providing significant insights that are anticipated to propel improvements in NLP technology and its application in many industries.http://www.sciencedirect.com/science/article/pii/S2772503024000598Deep learning techniquesNatural language processingSystematic review methodologiesText data analysisTransformer models |
| spellingShingle | Supriyono Aji Prasetya Wibawa Suyono Fachrul Kurniawan Advancements in natural language processing: Implications, challenges, and future directions Telematics and Informatics Reports Deep learning techniques Natural language processing Systematic review methodologies Text data analysis Transformer models |
| title | Advancements in natural language processing: Implications, challenges, and future directions |
| title_full | Advancements in natural language processing: Implications, challenges, and future directions |
| title_fullStr | Advancements in natural language processing: Implications, challenges, and future directions |
| title_full_unstemmed | Advancements in natural language processing: Implications, challenges, and future directions |
| title_short | Advancements in natural language processing: Implications, challenges, and future directions |
| title_sort | advancements in natural language processing implications challenges and future directions |
| topic | Deep learning techniques Natural language processing Systematic review methodologies Text data analysis Transformer models |
| url | http://www.sciencedirect.com/science/article/pii/S2772503024000598 |
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