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Journal articles on the topic 'NLP in Research'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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1

Bartkowiak, Judy. "Viewpoint: NLP in Qualitative Research." International Journal of Market Research 54, no. 4 (2012): 451–53. http://dx.doi.org/10.2501/ijmr-54-4-451-453.

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Wen, Bo. "Research on the applications of natural language processing." Applied and Computational Engineering 16, no. 1 (2023): 220–27. http://dx.doi.org/10.54254/2755-2721/16/20230896.

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In recent years, there has been a rapid advancement in natural language processing (NLP), leading to notable improvements in areas like sentiment analysis, machine translation, and text recognition. However, belonging to the same field under AI, the research materials and academic topics of NLP are not so adequate. Therefore, this paper introduces and shows the reader a general introduction to NLP for the current research environment, so that the reader can clearly understand the history of NLP, its uses, and the current and future research directions. It also analyzes the current mainstream a

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Erik, Cambria. "A Review of Natural Language Processing Research." Engineering Computations 2017, no. 10 (2017): 10. https://doi.org/10.5281/zenodo.1000805.

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Natural language processing (NLP) is a theory-motivated range of computational techniques for the automatic analysis and representation of human language. NLP research has evolved from the era of punch cards and batch processing (in which the analysis of a sentence could take up to 7 minutes) to the era of Google and the likes of it (in which millions of webpages can be processed in less than a second). This review paper draws on recent developments in NLP research to look at the past, present, and future of NLP technology in a new light. Borrowing the paradigm of ‘jumping curves’ from the fie

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Cai, Hanwen. "Research on the intersection of natural language processing and deep learning." Applied and Computational Engineering 42, no. 1 (2024): 61–66. http://dx.doi.org/10.54254/2755-2721/42/20230685.

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In the past ten years, Natural Language Processing (NLP) has made many surprising progress due to the rapid development of Deep Learning(DL) and further explored the possibility of future development. This article briefly introduces the NLP field, the basic structure of DL, and the impact of the combination of DL and NLP on the NLP field. Finally, it reviews the limitations of the two under the constraints of current science and technology and looks forward to the possibility and direction of their future development. Appropriately applying DL to NLP can indeed bring great progress to the core

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Sharma, Nakul, and Prasanth Yalla. "Developing Research Questions in Natural Language Processing and Software Engineering." JOIV : International Journal on Informatics Visualization 2, no. 4 (2018): 268. http://dx.doi.org/10.30630/joiv.2.4.159.

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This paper endeavors to develop newer medium of developing research questions by keeping in view both fields of SE and NLP in proper perspectives. An overview of the current state of art research in SE and NLP is presented. This is done by referring to the SE Body of Knowledge (SEBOK). Analogues to SEBOK, there are no separate Body of Knowledge available for the NLP/Computational Linguistics (CL). Hence whatever falls within the category of NLP/CL was considered in framing the research categories from the NLP/CL side. The paper concludes with future scope of the research presented.

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Alahmadi, Dimah, Amal Babour, Kawther Saeedi, and Anna Visvizi. "Ensuring Inclusion and Diversity in Research and Research Output: A Case for a Language-Sensitive NLP Crowdsourcing Platform." Applied Sciences 10, no. 18 (2020): 6216. http://dx.doi.org/10.3390/app10186216.

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In the context of the debate on the need to place citizens at the center of the technological revolution, this paper makes a case for a natural language processing (NLP) crowdsourcing platform that ensures inclusion and diversity, thus making the research outcome relevant and applicable across issues and domains. This paper also makes the case that by enabling participation for a wide variety of stakeholders, this NLP crowdsourcing platform might ultimately prove useful in the decision- and policy-making processes at city, community, and country levels. Against the backdrop of the debates on a

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Crossley, Scott A. "Advancing research in second language writing through computational tools and machine learning techniques: A research agenda." Language Teaching 46, no. 2 (2013): 256–71. http://dx.doi.org/10.1017/s0261444812000547.

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This paper provides an agenda for replication studies focusing on second language (L2) writing and the use of natural language processing (NLP) tools and machine learning algorithms. Specifically, it introduces a range of the available NLP tools and machine learning algorithms and demonstrates how these could be used to replicate seminal studies in L2 writing that concentrate on longitudinal writing development, predicting essay quality, examining differences between L1 and L2 writers, the effects of writing topics, and the effects of writing tasks. The paper concludes with implications for th

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Abram, Marissa D., Karen T. Mancini, and R. David Parker. "Methods to Integrate Natural Language Processing Into Qualitative Research." International Journal of Qualitative Methods 19 (January 1, 2020): 160940692098460. http://dx.doi.org/10.1177/1609406920984608.

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Background: Qualitative methods analyze contextualized, unstructured data. These methods are time and cost intensive, often resulting in small sample sizes and yielding findings that are complicated to replicate. Integrating natural language processing (NLP) into a qualitative project can increase efficiency through time and cost savings; increase sample sizes; and allow for validation through replication. This study compared the findings, costs, and time spent between a traditional qualitative method (Investigator only) to a method pairing a qualitative investigator with an NLP function (Inve

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Yu, Shun, Anh Le, Emily Feld, et al. "A Natural Language Processing–Assisted Extraction System for Gleason Scores: Development and Usability Study." JMIR Cancer 7, no. 3 (2021): e27970. http://dx.doi.org/10.2196/27970.

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Background Natural language processing (NLP) offers significantly faster variable extraction compared to traditional human extraction but cannot interpret complicated notes as well as humans can. Thus, we hypothesized that an “NLP-assisted” extraction system, which uses humans for complicated notes and NLP for uncomplicated notes, could produce faster extraction without compromising accuracy. Objective The aim of this study was to develop and pilot an NLP-assisted extraction system to leverage the strengths of both human and NLP extraction of prostate cancer Gleason scores. Methods We collecte

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Passmore, Jonathan, and Tatiana Rowson. "Neuro-linguistic programming: A review of NLP research and the application of NLP in coaching." International Coaching Psychology Review 14, no. 1 (2019): 57–69. http://dx.doi.org/10.53841/bpsicpr.2019.14.1.57.

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The huge popularity of neuro-linguistic programming (NLP) over the past three decades has in some ways mirrored the growth in coaching psychology. This paper is part of a series of four papers in a special issue within ICPR that aims to explore NLP coaching from diverse perspectives, offering personal insights or reviews of evidence. As part of this process a pair of authors were invited to advance the case for and the case against NLP. This paper aims to adopt a critical stance; reviewing the concept of NLP, exploring the claims made by advocates and critically reviewing the evidence from a p

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Jiang, Yunqing, Patrick Cheong-Iao Pang, Dennis Wong, and Ho Yin Kan. "Natural Language Processing Adoption in Governments and Future Research Directions: A Systematic Review." Applied Sciences 13, no. 22 (2023): 12346. http://dx.doi.org/10.3390/app132212346.

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Natural language processing (NLP), which is known as an emerging technology creating considerable value in multiple areas, has recently shown its great potential in government operations and public administration applications. However, while the number of publications on NLP is increasing steadily, there is no comprehensive review for a holistic understanding of how NLP is being adopted by governments. In this regard, we present a systematic literature review on NLP applications in governments by following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol

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Apta Mylsidayu, Ali Nina Liche Seniati, and Emanuel Taru Guritna. "BIBLIOMETRIC INSIGHTS INTO NLP RESEARCH: GLOBAL TRENDS AND FUTURE IMPLICATIONS FOR SPORTS PSYCHOLOGY." Gladi : Jurnal Ilmu Keolahragaan 16, no. 02 (2025): 282–99. https://doi.org/10.21009/gjik.162.13.

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Over the past four decades, the application of Neuro-Linguistic Programming (NLP) has expanded significantly, attracting considerable interest from researchers worldwide. Despite the proliferation of research across various disciplines, comprehensive reviews that map global trends in NLP research remain limited. Consequently, this study aims to evaluate the evolution and contributions of NLP research over time, and to identify the key topics, leading researchers, prominent institutions, and influential countries involved in its development. This study employs a literature review methodology us

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Kim, Chulho. "An Implementation of Natural Language Processing and Text Mining in Stroke Research." Journal of the Korean Neurological Association 39, no. 3 (2021): 121–28. http://dx.doi.org/10.17340/jkna.2021.3.2.

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Natural language processing (NLP) is a computerized approach to analyzing text that explores how computers can be used to understand and manipulate natural language text or speech to do useful things. In healthcare field, these NLP techniques are applied in a variety of applications, ranging from evaluating the adequacy of treatment, assessing the presence of the acute illness, and the other clinical decision support. After converting text into computer-readable data through the text preprocessing process, an NLP can extract valuable information using the rule-based algorithm, machine learning

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Odisho, Anobel Y., Mark Bridge, Mitchell Webb, et al. "Automating the Capture of Structured Pathology Data for Prostate Cancer Clinical Care and Research." JCO Clinical Cancer Informatics, no. 3 (December 2019): 1–8. http://dx.doi.org/10.1200/cci.18.00084.

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Purpose Cancer pathology findings are critical for many aspects of care but are often locked away as unstructured free text. Our objective was to develop a natural language processing (NLP) system to extract prostate pathology details from postoperative pathology reports and a parallel structured data entry process for use by urologists during routine documentation care and compare accuracy when compared with manual abstraction and concordance between NLP and clinician-entered approaches. Materials and Methods From February 2016, clinicians used note templates with custom structured data eleme

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Hupkes, Dieuwke, Mario Giulianelli, Verna Dankers, et al. "A taxonomy and review of generalization research in NLP." Nature Machine Intelligence 5, no. 10 (2023): 1161–74. http://dx.doi.org/10.1038/s42256-023-00729-y.

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AbstractThe ability to generalize well is one of the primary desiderata for models of natural language processing (NLP), but what ‘good generalization’ entails and how it should be evaluated is not well understood. In this Analysis we present a taxonomy for characterizing and understanding generalization research in NLP. The proposed taxonomy is based on an extensive literature review and contains five axes along which generalization studies can differ: their main motivation, the type of generalization they aim to solve, the type of data shift they consider, the source by which this data shift

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Leeson, William, Adam Resnick, Daniel Alexander, and John Rovers. "Natural Language Processing (NLP) in Qualitative Public Health Research: A Proof of Concept Study." International Journal of Qualitative Methods 18 (January 1, 2019): 160940691988702. http://dx.doi.org/10.1177/1609406919887021.

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Qualitative data-analysis methods provide thick, rich descriptions of subjects’ thoughts, feelings, and lived experiences but may be time-consuming, labor-intensive, or prone to bias. Natural language processing (NLP) is a machine learning technique from computer science that uses algorithms to analyze textual data. NLP allows processing of large amounts of data almost instantaneously. As researchers become conversant with NLP, it is becoming more frequently employed outside of computer science and shows promise as a tool to analyze qualitative data in public health. This is a proof of concept

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Zhao, Ziang. "Research on the evolution of neural networks models in NLP." Applied and Computational Engineering 4, no. 1 (2023): 690–95. http://dx.doi.org/10.54254/2755-2721/4/2023385.

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Since their first emergence, neural networks have produced significant results and have been an essential tool for solving natural language processing (NLP) problems. This paper aims to provide insight into the development of neural network models and their application in various NLP problems. The efficiency of the different approaches would be analyzed and insight into the different models would be provided. Overall, neural networks have been proven to be essential for solving problems related to NLP, and it is believed that further advancements in newer models would see a surge in performanc

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Lindvall, Charlotta, Elizabeth J. Lilley, Zara Cooper, et al. "Using natural language processing to assess palliative care processes in cancer patients receiving venting gastrostomy tube." Journal of Clinical Oncology 35, no. 31_suppl (2017): 7. http://dx.doi.org/10.1200/jco.2017.35.31_suppl.7.

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7 Background: Natural Language Processing (NLP) presents a novel method of extracting text-embedded information from the electronic health record (EHR) to improve routine assessment of palliative quality metrics such as timely advance care planning (ACP), palliative care provision (PC), and hospice referral. Methods: We identified cancer patients (ICD-9-CM codes 140-209) who received a gastrostomy tube (ICD-9-CM 43.11, 43.19, 44.32; CPT code 49440) from Jan 1, 2012, to Mar 31, 2016 at an academic medical center. We used NLP to identify palliative indication for gastrostomy tube placement by la

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G, Dr Chandrika. "Natural Language Processing (NLP)." International Journal for Research in Applied Science and Engineering Technology 12, no. 6 (2024): 1092–95. http://dx.doi.org/10.22214/ijraset.2024.63281.

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Abstract: Machines utilize Natural Language Processing (NLP), a subfield of Artificial Intelligence (AI), to comprehend, evaluate, and translate human languages. Due to advancements in information and communication technology over the last ten years, NLP has gained increased awareness and has been the subject of several studies. Understanding the steps required to advance literary understanding is so crucial. The study's objective is to provide an organized review of the literature in NLP research using bibliometric analysis. In the discipline of NLP, the study highlights important research fi

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Aggarwal, Dipanshu, and Kriti Pallavi. "Advancements and challenges in natural language processing in oral cancer research: A narrative review." Cancer Research, Statistics, and Treatment 7, no. 2 (2024): 228–33. http://dx.doi.org/10.4103/crst.crst_193_23.

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ABSTRACT Oral cancer is a serious and often fatal disease that requires early detection and treatment for improved outcomes. Natural language processing (NLP) has emerged as a promising tool for improving the diagnosis and management of oral cancer. In this review, we examine the advancements and challenges in NLP in oral cancer research. We comprehensively searched electronic databases of PubMed/MEDLINE, Scopus, and Web of Science by using search terms “natural language processing” AND “head and neck cancer” OR “oral cancer” OR “oral oncology” OR “oral squamous cell carcinoma” OR “oral potent

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Middleton, Rod, James Witts, Elaine Craig, Katherine Tuite-Dalton, Jeff Rodgers, and Richard Nicholas. "138 Done in 16 Seconds: extracting research ready data from NHS clinical letters." Journal of Neurology, Neurosurgery & Psychiatry 93, no. 9 (2022): e2.93. http://dx.doi.org/10.1136/jnnp-2022-abn2.182.

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BackgroundLetters dictated by healthcare professionals in routine patient care form an invaluable dataset but are difficult to access and interpret. The UK MS Register (UKMSR) previously outlined (ABN2019) usage of Natural Language Processing Algorithms (NLP-A) to harvest and transform written language into analysable data in databases. We expanded the variables captured, increased the number of donating hospitals and compared the results to the previous NLP-A.AimApply the new NLP-A to a random letter selection and evaluate output and results.MethodsA random, seeded, selection algorithm chose

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de Rijk, Lisa, Lucas A. C. Derks, Bruce Grimley, and Jaap Hollander. "The evidence for NLP." International Coaching Psychology Review 14, no. 1 (2019): 5–30. http://dx.doi.org/10.53841/bpsicpr.2019.14.1.5.

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After 45 years of strong development, global application and ongoing criticism, the contours of what constitutes NLP remain vague, to insiders and outsiders alike. NLP experts use more or less different definitions and criteria for the tools, techniques and foundation principles of NLP. This situation has made it nearly impossible to satisfy the request for research evidence of NLP’s effectiveness in coaching.The purpose of this paper therefore is to commence a discussion of the challenges facing NLP in gaining legitimacy as a coaching approach without an evidence base. The paper critiques the

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Subramanian, Naveen G., Hector Garcia Pleitez, Daniel Nguyen, et al. "Diagnostic performance of natural language processing in detection of acute cancer VTE." Journal of Clinical Oncology 41, no. 16_suppl (2023): e19062-e19062. http://dx.doi.org/10.1200/jco.2023.41.16_suppl.e19062.

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e19062 Background: The annual incidence of venous thromboembolism (VTE) is roughly 50-fold increased post allogeneic hematopoietic cell transplant (HSCT). Timely detection of VTE events has a significant impact on morbidity and mortality from these events. Manual chart review is commonly used for detection of VTE events; however, this is labor intensive and often not feasible. The International Classification of Diseases, 10th revision (ICD-10) has attempted to codify VTE more easily but has high false positive and false negative rates. Natural language processing (NLP) algorithms have been de

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Sekarsiwi, Anggraini Respati, and Arlina Dewi. "ACTION RESEARCH: INFLUENCE NEURO-LINGUISTIC PROGRAMMING (NLP) ON RESILIENCE AND STRESS OF CENTRAL SURGERY INSTALLATION NURSES." Media Penelitian dan Pengembangan Kesehatan 35, no. 1 (2025): 192–205. https://doi.org/10.34011/jmp2k.v35i1.2618.

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Perawat yang bekerja di instalasi bedah sentral kerap mengalami beban kerja yang tinggi termasuk fisik dan emosional serta paparan radiasi berbahaya yang dapat menyebabkan stress kerja dan kejenuhan. Neuro-Linguistic Programming (NLP) adalah pendekatan yang berfokus pada mengubah pola pikir dan perilaku untuk meningkatkan kemampuan coping stress dengan memodifikasi respons individu ketika stress. Penelitian ini bertujuan untuk menganalisis pengaruh Neuro-Linguistic Programming (NLP) terhadap resiliensi dan stres perawat instalasi bedah sentral di Rumah Sakit Umum Diponegoro Dua Satu Klaten. Pe

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Koskimaki, Jacob, Jenny Hu, Yiduo Zhang, et al. "Natural language processing-optimized case selection for real-world evidence studies." Journal of Clinical Oncology 40, no. 16_suppl (2022): 1556. http://dx.doi.org/10.1200/jco.2022.40.16_suppl.1556.

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1556 Background: Much information describing a patient’s cancer treatment remains in unstructured text in electronic health records and is not recorded in discrete data fields. Accurate data completeness is essential for quality care improvement and research studies on de-identified patient records. Accessing this high-value content often requires manual and extensive curation review. Methods: AstraZeneca, CancerLinQ, ConcertAI, and Tempus have developed a natural language processing (NLP)-assisted process to improve clinical cohort selection for targeted curation efforts. Hybrid, machine-lear

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Grimley, Bruce Nerli. "What is NLP? The development of a grounded theory of Neuro-Linguistic Programming, (NLP), within an action research journey. Implications for the use of NLP in coaching psychology." International Coaching Psychology Review 11, no. 2 (2016): 166–78. http://dx.doi.org/10.53841/bpsicpr.2016.11.2.166.

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Despite the wide use of Neuro-Linguistic Programming, (NLP), within coaching psychology very little literature of a critical nature examines what NLP is, how it works and whether it works. This paper seeks to address that void by asking 15 subject matter experts what their definition of NLP is. In order to develop a theory of NLP that was not skewed, data from a discussion between 19 NLP informed professionals concerning the authenticity of NLP, (44,000 words), was also used in the coding process to generate a grounded theory of NLP. The emerging theory was recycled back to the 15 subject matt

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Al-Khalifa, Hend S., Taif AlOmar, and Ghala AlOlyyan. "Natural Language Processing Patents Landscape Analysis." Data 9, no. 4 (2024): 52. http://dx.doi.org/10.3390/data9040052.

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Understanding NLP patents provides valuable insights into innovation trends and competitive dynamics in artificial intelligence. This study uses the Lens patent database to investigate the landscape of NLP patents. The overall patent output in the NLP field on a global scale has exhibited a rapid growth over the past decade, indicating rising research and commercial interests in applying NLP techniques. By analyzing patent assignees, technology categories, and geographic distribution, we identify leading innovators as well as research hotspots in applying NLP. The patent landscape reflects int

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Idnay, Betina, Caitlin Dreisbach, Chunhua Weng, and Rebecca Schnall. "A systematic review on natural language processing systems for eligibility prescreening in clinical research." Journal of the American Medical Informatics Association 29, no. 1 (2021): 197–206. http://dx.doi.org/10.1093/jamia/ocab228.

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Abstract Objective We conducted a systematic review to assess the effect of natural language processing (NLP) systems in improving the accuracy and efficiency of eligibility prescreening during the clinical research recruitment process. Materials and Methods Guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) standards of quality for reporting systematic reviews, a protocol for study eligibility was developed a priori and registered in the PROSPERO database. Using predetermined inclusion criteria, studies published from database inception through February

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Baskara, Risang. "NATURAL LANGUAGE PROCESSING-BASED BLENDED LEARNING IN THE EFL FLIPPED CLASSROOM: A CONCEPTUAL FRAMEWORK AND PROPOSED DESIGNS." Abjadia : International Journal of Education 9, no. 1 (2024): 1–17. http://dx.doi.org/10.18860/abj.v9i1.18795.

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The use of Natural Language Processing (NLP) in the English as a Foreign Language (EFL) flipped classroom has the potential to support blended learning and enhance learner autonomy and language development. However, there is a need for more research on the use of NLP in the EFL flipped classroom. This paper addresses this research gap by proposing a conceptual framework for using NLP in the EFL flipped classroom and a set of proposed designs and activities incorporating NLP to enhance learner autonomy and language development. This paper aims to provide a conceptual framework for using NLP in

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Binti Mustaffa, Siti Nur Fathin Najwa, Jamaludin Bin Sallim, and Rozlina Binti Mohamed. "Exploring the Synergy: User Stories in Agile Software Development." International Journal of Engineering, Science and Information Technology 5, no. 3 (2025): 480–90. https://doi.org/10.52088/ijesty.v5i3.1161.

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User Stories are commonly used artifacts to capture user requirements in Agile Software Development (ASD). They are short, semi-structured statements that describe requirements. Natural Language Processing (NLP) techniques can be advantageous for research on user stories. This paper investigates User Stories and NLP about their applications, critically examines existing research approaches related to NLP in user stories, presents the challenges and suggested future work. Relevant papers were obtained from well-recognized digital libraries and scientific databases, including ScienceDirect, Scop

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Jawad, Aqeel Mahmood, Lujain Maher AlNuaemy, Nameer Hashim Qasim, and Oleksandr Lyashenko. "PECULIARITIES OF USING NEURO-LINGUISTIC PROGRAMMING FOR THE REHABILITATION OF SERVICEMEN WHO WERE IN ARMED CONFLICTS." Development of Management and Entrepreneurship Methods on Transport (ONMU) 84, no. 3 (2023): 40–55. https://doi.org/10.31375/2226-1915-2023-3-40-55.

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Neuro-linguistic programming (NLP) has been getting a lot of interest lately for its potential role in the recovery of war veterans. When it comes to treating mental and emotional health problems, neuro-linguistic programming (NLP) is one of the most effective methods because of the way it makes use of language and communication patterns.The nuances of using NLP for the recovery of combat veterans are examined in this research. The research looks at how well NLP works for treating Post-traumatic stress disorder (PTSD), depression, anxiety, and other mental and emotional wounds that might resul

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Mashima, Yukinori, Takashi Tamura, Jun Kunikata, et al. "Using Natural Language Processing Techniques to Detect Adverse Events From Progress Notes Due to Chemotherapy." Cancer Informatics 21 (January 2022): 117693512210850. http://dx.doi.org/10.1177/11769351221085064.

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Objective: In recent years, natural language processing (NLP) techniques have progressed, and their application in the medical field has been tested. However, the use of NLP to detect symptoms from medical progress notes written in Japanese, remains limited. We aimed to detect 2 gastrointestinal symptoms that interfere with the continuation of chemotherapy—nausea/vomiting and diarrhea—from progress notes using NLP, and then to analyze factors affecting NLP. Materials and methods: In this study, 200 patients were randomly selected from 5277 patients who received intravenous injections of cytoto

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S., Panchal, and Thakur P. "HARNESSING THE POWER OF NATURAL LANGUAGE PROCESSING IN NURSING SERVICES." International Journal of Advanced Research 12, no. 05 (2024): 154–56. http://dx.doi.org/10.21474/ijar01/18697.

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Natural Language Processing (NLP) has emerged as a transformative force in healthcare, revolutionizing various aspects of nursing services. This research article explores the applications, benefits, challenges, and future directions of NLP in nursing. Through a comprehensive review, this article highlights the pivotal role of NLP in clinical decision support, health records management, patient interaction, and research synthesis. Real-world case studies illustrate the tangible impact of NLP on improving efficiency, accuracy, and patient outcomes. While acknowledging the advantages of NLP, the

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Gopi, Krishna. "Reinforcement Learning based NLP." International Journal of Soft Computing and Engineering (IJSCE) 13, no. 4 (2023): 1–4. https://doi.org/10.35940/ijsce.J0476.0913423.

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In the field of Natural Language Processing (NLP), reinforcement learning (RL) has drawn attention as a viable method for training models. An agent is trained to interact with a linguistic environment in order to carry out a given task using RL- based NLP, and the agent learns from feedback in the form of rewards or penalties. This method has been effectively used for a variety of linguistic problems, including text summarization, conversation systems, and machine translation. Sequence-to- sequence Two common methods used in RL-based NLP are reinforcement learning and deep reinforcement learni

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Grimley, Bruce. "The need for neuro-linguistic programming to develop greater construct validity." International Coaching Psychology Review 14, no. 1 (2019): 31–44. http://dx.doi.org/10.53841/bpsicpr.2019.14.1.31.

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AbstractThis paper tracks a practitioner’s journey through his attempts to understand the nature of neuro-linguistic programming (NLP). It draws from the author’s self-study, being both a master NLP trainer and a chartered psychologist and also from the author’s own PhD research, (Grimley, 2016) which explicitly asked the question ‘What is NLP?’ The author discusses the importance of finding an answer to this question should NLP as a field, and its application to coaching specifically, wish to validate its modality. Taking from psychometric literature the idea of construct validity, the author

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Shradha Bohra and Nikita Shukla. "Cracking the code: A research study on neuro-linguistic programming (NLP) and its impact on organizational success." International Journal of Science and Research Archive 9, no. 2 (2023): 090–95. http://dx.doi.org/10.30574/ijsra.2023.9.2.0521.

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Neuro-Linguistic Programming (NLP) is a valuable technique for enhancing communication skills, motivation, and mentality in organizations. It helps individuals build rapport, understand others' perspectives, and convey messages with clarity and impact. NLP can help individuals achieve personal goals that go beyond monetary and performance targets, boost employee morale, loyalty, effort, and profitability. The study uses a secondary research method, which involves using existing data and research reports. The data required for conducting the study is available online, such as websites, articles

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Zinzurade, Akshada. "Data Augmentation in NLP: Concepts, Classifications, and Research Challenges A Deep Dive into Data Augmentation Strategies for Robust NLP Systems." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem49548.

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Abstract - Natural Language Processing (NLP) has witnessed significant advancements due to the rise of deep learning techniques. However, most NLP models require large annotated datasets to perform effectively. Creating such datasets is expensive and time-consuming. Data augmentation offers a solution by artificially expanding training data, improving model robustness and generalization. This paper presents a comprehensive overview of data augmentation in NLP, outlining its key concepts, classification strategies, and real-world applications. Further, it highlights ongoing research challenges

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Krishna, Gopi. "Reinforcement Learning based NLP." International Journal of Soft Computing and Engineering 13, no. 4 (2023): 1–4. http://dx.doi.org/10.35940/ijsce.j0476.0913423.

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In the field of Natural Language Processing (NLP), reinforcement learning (RL) has drawn attention as a viable method for training models. An agent is trained to interact with a linguistic environment in order to carry out a given task using RL- based NLP, and the agent learns from feedback in the form of rewards or penalties. This method has been effectively used for a variety of linguistic problems, including text summarization, conversation systems, and machine translation. Sequence-to- sequence Two common methods used in RL-based NLP are reinforcement learning and deep reinforcement learni

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Chang, Tammy, Melissa DeJonckheere, V. G. Vinod Vydiswaran, Jiazhao Li, Lorraine R. Buis, and Timothy C. Guetterman. "Accelerating Mixed Methods Research With Natural Language Processing of Big Text Data." Journal of Mixed Methods Research 15, no. 3 (2021): 398–412. http://dx.doi.org/10.1177/15586898211021196.

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Situations of catastrophic social change, such as COVID-19, raise complex, interdisciplinary research questions that intersect health, education, economics, psychology, and social behavior and require mixed methods research. The pandemic has been a quickly evolving phenomenon, which pressures the time necessary to perform mixed methods research. Natural language processing (NLP) is a promising solution that leverages computational approaches to analyze textual data in “natural language.” The aim of this article is to introduce NLP as an innovative technology to assist with the rapid mixed meth

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S, Harshada. "CYBERBULLYING DETECTION BY NLP." International Scientific Journal of Engineering and Management 04, no. 06 (2025): 1–9. https://doi.org/10.55041/isjem04059.

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Abstract - Cyberbullying has become a growing concern with the rise of social media and online communication platforms. Manual moderation techniques are inadequate to handle the volume and complexity of abusive content. This study presents a cyberbullying detection model using Natural Language Processing (NLP) techniques. The system analyzes user-generated text to identify bullying behavior using pre-trained transformers and machine learning algorithms. Evaluation on benchmark datasets demonstrates high accuracy and low false positives. The research highlights the potential for real-time deplo

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Egan, Kathryn S., Gary H. Lyman, Karma L. Kreizenbeck, et al. "Measuring adherence to a Choosing Wisely recommendation in a regional oncology clinic." Journal of Clinical Oncology 34, no. 7_suppl (2016): 196. http://dx.doi.org/10.1200/jco.2016.34.7_suppl.196.

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196 Background: Natural language processing (NLP) has the potential to significantly ease the burden of manual abstraction of unstructured electronic text when measuring adherence to national guidelines. We incorporated NLP into standard data processing techniques such as manual abstraction and database queries in order to more efficiently evaluate a regional oncology clinic’s adherence to ASCO’s Choosing Wisely colony stimulating factor (CSF) recommendation using clinical, billing, and cancer registry data. Methods: Database queries on the clinic’s cancer registry yielded the study population

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Agrawal, Smita, Rohini George, Vivek Prabhakar Vaidya, et al. "Development of natural language processing (NLP) models for extracting key features from unstructured notes to create real-world data (RWD) assets for clinical research at scale." Journal of Clinical Oncology 41, no. 16_suppl (2023): 6607. http://dx.doi.org/10.1200/jco.2023.41.16_suppl.6607.

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6607 Background: RWD derived from Electronic Health Records (EHR) has detailed clinical information about patient journeys that can assist in clinical research, trial design, safety assessments etc. However, much of the vital information is locked away in unstructured clinical texts and needs to be converted to structured format to be useful for downstream applications. We demonstrate how this can be achieved at scale with a high degree of accuracy through NLP. Methods: NLP models were developed to extract data for 11 clinical variables from unstructured notes of ~98k lung cancer patients and

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Bi, Yifan. "Chinese news topic prediction using bidirectional encoder representation from transformers." Theoretical and Natural Science 18, no. 1 (2023): 133–39. http://dx.doi.org/10.54254/2753-8818/18/20230358.

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Nowadays, there are many researches on natural language processing (NLP). Through the research of NLP method, many problems in machine learning field have been solved. However, since the study of Chinese NLP has not developed rapidly until recent years, there is still much to be studied on Chinese NLP. As an excellent pre-training model, whether Bidirectional Encoder Representation from Transformers (BERT) performs well on specific Chinese NLP remains to be studied. Therefore, this paper uses BERT for Chinese NLP, and trains BERT model by collecting news title data to achieve Chinese text clas

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Lent, Heather, Erick Galinkin, Yiyi Chen, Jens Myrup Pedersen, Leon Derczynski, and Johannes Bjerva. "NLP Security and Ethics, in the Wild." Transactions of the Association for Computational Linguistics 13 (2025): 709–43. https://doi.org/10.1162/tacl_a_00762.

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Abstract As NLP models are used by a growing number of end-users, an area of increasing importance is NLP Security (NLPSec): assessing the vulnerability of models to malicious attacks and developing comprehensive countermeasures against them. While work at the intersection of NLP and cybersecurity has the potential to create safer NLP for all, accidental oversights can result in tangible harm (e.g., breaches of privacy or proliferation of malicious models). In this emerging field, however, the research ethics of NLP have not yet faced many of the long-standing conundrums pertinent to cybersecu

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Pfaff, Emily R., Miles Crosskey, Kenneth Morton, and Ashok Krishnamurthy. "Clinical Annotation Research Kit (CLARK): Computable Phenotyping Using Machine Learning." JMIR Medical Informatics 8, no. 1 (2020): e16042. http://dx.doi.org/10.2196/16042.

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Computable phenotypes are algorithms that translate clinical features into code that can be run against electronic health record (EHR) data to define patient cohorts. However, computable phenotypes that only make use of structured EHR data do not capture the full richness of a patient’s medical record. While natural language processing (NLP) methods have shown success in extracting clinical features from text, the use of such tools has generally been limited to research groups with substantial NLP expertise. Our goal was to develop an open-source phenotyping software, Clinical Annotation Resea

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Singh, Ankit Kumar. "Desktop Assistant Based on NLP." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem34539.

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Natural Language Processing (NLP) has emerged as a critical component of artificial intelligence, enabling machines to comprehend and interact with human language. This research paper explores the current state of the art in NLP, highlighting recent innovations, trends, and ongoing challenges. It delves into various applications of NLP, discusses the datasets and models that drive advancements, and examines the evaluation metrics used to assess NLP systems. Key innovations such as transformers, pre-trained language models, and transfer learning have revolutionized the field, leading to signifi

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R. Othman, Arazoo. "NLP as Individual Programming:." Journal of University of Raparin 11, no. 2 (2024): 809–37. http://dx.doi.org/10.26750/vol(11).no(2).paper33.

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The current study is an attempt to analyze two literary texts, namely William Shakespeare's play Hamlet and Bachtyar Ali's Kurdish novel The City of White Musicians, in the light of, neurolinguistics as an individual programming. These are from two distinct literary traditions that were picked because they essentially share the appearance of ghosts. To find out how these subtleties affect the reader's understanding, neurolinguistics is applied. Consequently, the paper's title was chosen. The texts serve as a sort of manual, and our goal is to identify the link that connects language and reason

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Praveen Kumar Gupta, Gaurav Dubey, and Akshat Sharma. "Text Summarization Using NLP." International Research Journal on Advanced Engineering Hub (IRJAEH) 3, no. 05 (2025): 2665–70. https://doi.org/10.47392/irjaeh.2025.0395.

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This paper provides a detailed view of how the development and usage of a natural language processing text summarizer have occurred. Based on its requirement to present condensed yet meaningful conclusions without affecting the original meaning of the given data, this system is built up. Techniques utilized, like feature extraction, preprocessing of the given data, and summarization itself, have also been mentioned within this research paper. It also reviews how well the system performs in benchmark datasets, and it expounds on some of its applications, limitations, and possible further develo

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Wu, Haokun. "Large language models capsule: A research analysis of In-Context Learning (ICL) and Parameter-Efficient Fine-Tuning (PEFT) methods." Applied and Computational Engineering 43, no. 1 (2024): 327–31. http://dx.doi.org/10.54254/2755-2721/43/20230858.

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In the context of natural language processing (NLP), this paper addresses the growing need for efficient adaptation techniques for pre-trained language models. It begins by summarizing the current landscape of NLP, highlighting the challenges associated with fine-tuning large language models like BERT and Transformer. The paper then introduces and analyzes three categories of parameter-efficient fine-tuning (PEFT) approaches, namely, In-Context Learning (ICL)-inspired Fine-Tuning, Low-Rank Adaptation PEFTs (LoRA), and Activation-based PEFTs. Within these categories, it explores techniques such

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Gupta, B. M., S. M. Dhawan, and Ghouse Modin N. Mamdapur. "Research trends in the field of natural language processing : A scientometric study based on global publications during 2001-2020." COLLNET Journal of Scientometrics and Information Management 17, no. 1 (2023): 61–79. http://dx.doi.org/10.47974/cjsim-2022-0023.

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The study provides a quantitative and qualitative description of global research in “Natural Language Processing” ( NLP) using bibliometric methods. The analysis is based on publications data sourced from Scopus database for the period 2001-2020. The purpose of the study is to understand the status of NLP research at the global, national, institutional, and author level. The study highlights the productivity and performance of NLP research on a series of metrics as well as provides a visual view of collaborative network relationship between authors, research institutions, and leading countries

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