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Journal articles on the topic 'Natural Language Processes (NLP)'

Author: Grafiati

Published: 2 June 2025

Last updated: 19 July 2025

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1

Alharbi, Mohammad, Matthew Roach, Tom Cheesman, and Robert S. Laramee. "VNLP: Visible natural language processing." Information Visualization 20, no. 4 (2021): 245–62. http://dx.doi.org/10.1177/14738716211038898.

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In general, Natural Language Processing (NLP) algorithms exhibit black-box behavior. Users input text and output are provided with no explanation of how the results are obtained. In order to increase understanding and trust, users value transparent processing which may explain derived results and enable understanding of the underlying routines. Many approaches take an opaque approach by default when designing NLP tools and do not incorporate a means to steer and manipulate the intermediate NLP steps. We present an interactive, customizable, visual framework that enables users to observe and pa

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Researcher. "PROMPT ENGINEERING: REVOLUTIONIZING NATURAL LANGUAGE PROCESSING." International Journal of Artificial Intelligence and Machine Learning (IJAIML) 3, no. 2 (2024): 195–203. https://doi.org/10.5281/zenodo.13933394.

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Natural Language Processing (NLP) has undergone a revolution with the emergence of large language models (LLMs) like GPT and BERT. Prompt engineering, the art of crafting effective inputs to guide LLMs, has become a cornerstone of modern NLP applications. This paper explores how prompt engineering is streamlining NLP processes, revolutionizing industries such as banking, and addressing the inherent risks associated with generative AI. Through practical examples and a discussion of potential pitfalls, we offer strategies for mitigating challenges and maximizing the benefits of prompt engineerin

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Dallo, Khan Ali Marwani. "Natural language processing for business analytics." Advances in Engineering Innovation 3, no. 1 (2023): 37–40. http://dx.doi.org/10.54254/2977-3903/3/2023038.

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Natural Language Processing (NLP), a branch of artificial intelligence, is gaining traction as a potent tool for business analytics. With the proliferation of unstructured textual data, businesses are actively seeking methodologies to distill valuable insights from vast textual repositories. The introduction of NLP in the realm of business analytics offers a transformative approach, automating traditional manual processes and fostering real-time, data-driven decision-making. From sentiment analysis to text summarization, NLP is facilitating businesses in deciphering consumer feedback, predicti

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Abjalova, Manzura. "Lemmatization and stemming processes in automatic morphological analysis of Uzbek mants." Uzbekistan: language and culture 3, no. 3 (2024): 6–21. https://doi.org/10.52773/tsuull.uzlc.2024.3/hvmn8509.

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In the field of natural language processing, the stages of graphematic analysis (tokenization), morphological analysis (lemmatization and stemming), syntactic analysis (parsing), and semantic analysis are important for almost all areas of NLP. It is possible to create many software applications from processed natural language for digital technology. In NLP, the lemmatization and stemming technologies of morphological analysis are common to all languages, and they determine the normal form of word forms in the dictionary. Although the task of lemmatization and stemming is the same, they differ

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Simanullang, Gerald Shan Benediktus, and Jin Ai The. "Roles of Natural Language Processing in New Product Development Process: Literature Review." Jurnal Rekayasa Sistem Industri 13, no. 1 (2024): 117–30. http://dx.doi.org/10.26593/jrsi.v13i1.6790.117-130.

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Customer satisfaction is a key success factor for a business. To provide products that meet customer satisfaction, companies must be able to understand the customers’ needs and desires. Technological developments nowadays have helped companies to understand customer desires more easily so that companies can provide products that satisfy their customer. Natural Language Processing (NLP) is a technology that allows computers to process human language. NLP is also commonly referred as text-mining. NLP has been utilized in the New Product Development (NPD) process. We compiled studies related to N

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Khan, Noel, David Elizondo, Lipika Deka, and Miguel A. Molina-Cabello. "Natural Language Processing Tools and Workflows for Improving Research Processes." Applied Sciences 14, no. 24 (2024): 11731. https://doi.org/10.3390/app142411731.

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The modern research process involves refining a set of keywords until sufficiently pertinent results are obtained from acceptable sources. References and citations from the most relevant results can then be traced to related works. This process iteratively develops a set of keywords to find the most relevant literature. However, because a keyword-based search essentially samples a corpus, it may be inadequate for capturing a broad or exhaustive understanding of a topic. Further, a keyword-based search is dependent upon the underlying storage and retrieval technology and is essentially a syntac

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Hasan, Zohaib, Zeba Vishwakarma, and Nidhi Pateriya. "NLP and its Components: A Detailed Discussion." International Journal of Innovative Research in Computer and Communication Engineering 11, no. 09 (2023): 10798–803. http://dx.doi.org/10.15680/ijircce.2023.1109034.

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Natural Language Processing (NLP) encompasses computational techniques for processing and analyzing human language, primarily through Natural Language Understanding (NLU) and Natural Language Generation (NLG). NLU focuses on interpreting language by analyzing phonology (sounds), morphology (word structures), syntax (sentence structures), semantics (meaning), and pragmatics (context). These processes enable machines to comprehend the nuances of human language for accurate interpretation and response generation. NLG, in contrast, involves producing human-like text from structured data. It includ

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S.Girirajan. "Code Generation Empowered by Natural Language Processing and Machine Learning Algorithms." Advances in Nonlinear Variational Inequalities 28, no. 1s (2024): 44–56. http://dx.doi.org/10.52783/anvi.v28.2186.

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The goal of this study is to revolutionize code creation processes by investigating the synergistic union of machine learning (ML) and natural language processing (NLP). Enterprising non-programmers with entrance barriers, traditional approaches to code generation frequently demand expert-level programming expertise. Development teams can communicate coding tasks in natural language by utilizing NLP techniques like language modeling and semantic parsing. This helps to close the gap between human intent and instructions that can be executed by a computer. By incorporating ML techniques, the sys

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Chung, Joohyun, Sangmin Song, and Heesook Son. "Exploring Natural Language Processing through an Exemplar Using YouTube." International Journal of Environmental Research and Public Health 21, no. 10 (2024): 1357. http://dx.doi.org/10.3390/ijerph21101357.

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There has been a growing emphasis on data across various health-related fields, not just in nursing research, due to the increasing volume of unstructured data in electronic health records (EHRs). Natural Language Processing (NLP) provides a solution by transforming this unstructured data into structured formats, thereby facilitating valuable insights. This methodology paper explores the application of NLP in nursing, using an exemplar case study that analyzes YouTube data to investigate social phenomena among adults living alone. The methodology involves five steps: accessing data through You

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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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Sivakumar, R. D. Assistant Professor Department of Computer Science. "TEXT MINING AND NATURAL LANGUAGE PROCESSING FOR DECISION SUPPORT SYSTEMS." Indian Journal of Research and Development Systems in Technologization 1, no. 1 (2024): 14–23. https://doi.org/10.5281/zenodo.10847090.

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<em>The techniques of text mining and natural language processing (NLP) have been discovered to be of the essence to the concept of decision support systems (DSS). Due to the growing volume of organizational text data that encompasses from customer feedback to social media interactions, over viewing the desirable findings from unstructured texts becomes imperative. This work is intended to explain the use of text mining and NLP techniques to support and improve decision-making processes in different disciplines. Employing methods like sentiment analysis, topic modeling and named entity recogni

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Pawade, Premchand. "Resume Analysis System Using Natural Language Processing." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem49553.

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Abstract This project develops a resume analysis system using Natural Language Processing (NLP) to streamline hiring. Companies often receive large volumes of resumes, making it challenging to identify the best candidates quickly. Manually screening resumes is time-consuming, can be inconsistent, and may overlook key details. By automating this process with NLP, our system reads and evaluates resumes efficiently. It extracts key information like skills, experience, and education to match candidates to job requirements. For example, if a job requires a specific skill, the system can highlight c

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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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Besedina, A. Yu. "Evolution of natural language processing methods." Philosophical Problems of IT & Cyberspace (PhilIT&C), no. 2 (January 14, 2025): 52–63. https://doi.org/10.17726/philit.2024.2.4.

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Natural language processing (NLP) has undergone significant changes in its methods, reflecting advances in computing technology and cognitive research. This article reviews the key stages of the evolution of natural language processing methods. The article touches on the topic of the first NLP systems developed, provides justification for the reasons for the complexity of some processed texts and the possible depth of analysis. In addition, it describes not only NLP methods before and after the GPT revolution, but also current trends and prospects in the field of natural language processing. T

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Moaiad, Yazeed Al, Mohammad Alobed, Mahmoud Alsakhnini, and Alaa M. Momani. "Challenges in natural Arabic language processing." Edelweiss Applied Science and Technology 8, no. 6 (2024): 4700–4705. http://dx.doi.org/10.55214/25768484.v8i6.3018.

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Speech recognition and text summarization, plagiarism detection, machine translation, chatbots, sentiment analysis (SA), question answering (QA), and dialogue systems are all products of natural language processing (NLP), a branch of AI concerned with modeling natural languages for the purpose of developing relevant applications. NLP draws on several disciplines, not only computer science and linguistics, for its research and development. These include cognitive science, psychology, mathematics, and more. More than 1.5 billion Muslims throughout the world depend on the Arabic language for thei

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Zhao, Liping, Waad Alhoshan, Alessio Ferrari, et al. "Natural Language Processing for Requirements Engineering." ACM Computing Surveys 54, no. 3 (2021): 1–41. http://dx.doi.org/10.1145/3444689.

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Natural Language Processing for Requirements Engineering (NLP4RE) is an area of research and development that seeks to apply natural language processing (NLP) techniques, tools, and resources to the requirements engineering (RE) process, to support human analysts to carry out various linguistic analysis tasks on textual requirements documents, such as detecting language issues, identifying key domain concepts, and establishing requirements traceability links. This article reports on a mapping study that surveys the landscape of NLP4RE research to provide a holistic understanding of the field.

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Shynkarenko, Viktor, and Olena Kuropiatnyk. "Constructive Model of the Natural Language." Acta Cybernetica 23, no. 4 (2018): 995–1015. http://dx.doi.org/10.14232/actacyb.23.4.2018.2.

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The paper deals with the natural language model. Elements of the model (the language constructions) are images with such attributes as sounds, letters, morphemes, words and other lexical and syntactic components of the language. Based on the analysis of processes of the world perception, visual and associative thinking, the operations of formation and transformation of images are pointed out. The model can be applied in the semantic NLP.

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Chang, Kuei-Hu. "Natural Language Processing: Recent Development and Applications." Applied Sciences 13, no. 20 (2023): 11395. http://dx.doi.org/10.3390/app132011395.

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Guarasci, Raffaele, Giuseppe De Pietro, and Massimo Esposito. "Quantum Natural Language Processing: Challenges and Opportunities." Applied Sciences 12, no. 11 (2022): 5651. http://dx.doi.org/10.3390/app12115651.

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The meeting between Natural Language Processing (NLP) and Quantum Computing has been very successful in recent years, leading to the development of several approaches of the so-called Quantum Natural Language Processing (QNLP). This is a hybrid field in which the potential of quantum mechanics is exploited and applied to critical aspects of language processing, involving different NLP tasks. Approaches developed so far span from those that demonstrate the quantum advantage only at the theoretical level to the ones implementing algorithms on quantum hardware. This paper aims to list the approac

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Hutchinson, Tim. "Natural language processing and machine learning as practical toolsets for archival processing." Records Management Journal 30, no. 2 (2020): 155–74. http://dx.doi.org/10.1108/rmj-09-2019-0055.

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Purpose This study aims to provide an overview of recent efforts relating to natural language processing (NLP) and machine learning applied to archival processing, particularly appraisal and sensitivity reviews, and propose functional requirements and workflow considerations for transitioning from experimental to operational use of these tools. Design/methodology/approach The paper has four main sections. 1) A short overview of the NLP and machine learning concepts referenced in the paper. 2) A review of the literature reporting on NLP and machine learning applied to archival processes. 3) An

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Saeteros, David, David Gallardo-Pujol, and Daniel Ortiz-Martínez. "Text speaks louder: Insights into personality from natural language processing." PLOS One 20, no. 6 (2025): e0323096. https://doi.org/10.1371/journal.pone.0323096.

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In recent years, advancements in natural language processing (NLP) have enabled new approaches to personality assessment. This article presents an interdisciplinary investigation that leverages explainable AI techniques, particularly Integrated Gradients, to scrutinize NLP models’ decision-making processes in personality assessment and verify their alignment with established personality theories. We compare the effectiveness of typological (MBTI) and dimensional (Big Five) models, utilizing the Essays and MBTI datasets. Our methodology applies log-odds ratio with Informative Dirichlet Prior (I

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JosephNg, Poh Soon, Cheng Kian Wong, Koo Yuen Phan, and Jianhua Sun. "Natural Language Processing Stock Prediction Model Inclusion Innovation." Journal of Advanced Research in Applied Sciences and Engineering Technology 65, no. 2 (2025): 153–75. https://doi.org/10.37934/araset.65.2.153175.

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In the burgeoning era of technology, Artificial Technology plays a pivotal role across various sectors, including the financial market for a responsive institution. With the implementation of AI tools, the financial market is expected to function more efficiently while simultaneously reducing costs and time. The financial industry, grappling with biases in stock analysis and limited stock prediction tools, seeks an integrated solution merging technical analysis with current information through advancements like Natural Language Processing (NLP) to enhance the accuracy and efficiency of stock t

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Ratianantitra, Volatiana Marielle. "A State of the Art Review on Natural Language Processing applied to the Malagasy Language." International Conference on Artificial Intelligence and its Applications 2023 (November 9, 2023): 1–5. http://dx.doi.org/10.59200/icarti.2023.001.

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Based on the growing mass of information of all kinds to be processed and to facilitate human/machine dialogue, research teams and language industries are still developing applications integrating automatic natural language processing techniques. Natural Language Processing (NLP) is a discipline on the border of linguistics and computer science that concerns the application of computer programs and techniques to all aspects of human language. NLP is an area of research that is still open. It has extended its applications in various fields and applied them in several languages worldwide. This p

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Renuka, Balireddi* B. Nagamani P. Umadevi. "Exploring the Impact of Natural Language Processing in Clinical Trials, Regulatory, Healthcare Efficiency, and Drug Discovery Processes." International Journal of Pharmaceutical Sciences 2, no. 12 (2024): 495–506. https://doi.org/10.5281/zenodo.14280626.

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Natural Language Processing (NLP) has emerged as a transformative technology in pharmaceutical research and drug development, offering significant potential to enhance efficiency, reduce costs, and accelerate timelines. This paper explores the application of NLP in the pharmaceutical industry, focusing on its current trends, emerging technologies, and future directions. NLP techniques, including text mining, sentiment analysis, and information extraction, are increasingly being used to analyze vast amounts of unstructured data from clinical trial reports, scientific literature, regulatory docu

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Tkachenko, Kostiantyn. "Using of NLP Methods in Intelligent Educational Systems." Digital Platform: Information Technologies in Sociocultural Sphere 7, no. 1 (2024): 80–96. http://dx.doi.org/10.31866/2617-796x.7.1.2024.307009.

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For the effective organisation of educational processes supported by relevant intelligent learning systems, it is important to choose the right technologies that would ensure individualisation of learning, adequate perception of learning content, and the so-called “understanding” of texts in Ukrainian provided by students (description of the solution to a task, answers provided in their own words, not selected from the test answer options, questions to the system, etc.), prototyping, constant iteration during natural language text recognition and processing, and maximum reliability and efficie

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Sonali Kothari. "Leveraging natural language processing for automated regulatory compliance in financial reporting." Global Journal of Engineering and Technology Advances 23, no. 3 (2025): 091–99. https://doi.org/10.30574/gjeta.2025.23.3.0187.

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Natural Language Processing (NLP) is revolutionizing regulatory compliance in the financial sector by automating the interpretation and implementation of complex regulatory frameworks. Financial institutions face mounting challenges in parsing extensive regulatory requirements amid continuously evolving Basel III, Dodd-Frank, and FASB guidelines. This article explores how financial institutions can leverage NLP technologies to transform traditional manual compliance processes into automated, efficient systems. Through advanced techniques including domain-specific language models, semantic anal

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Tkachenko, Kostiantyn. "Using of NLP Methods in Intelligent Educational Systems." Digital Platform: Information Technologies in Sociocultural Sphere 7, no. 1 (2024): 80–96. https://doi.org/10.31866/2617-796X.7.1.2024.307009.

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For the effective organisation of educational processes supported by relevant intelligent learning systems, it is important to choose the right technologies that would ensure individualisation of learning, adequate perception of learning content, and the so-called “understanding” of texts in Ukrainian provided by students (description of the solution to a task, answers provided in their own words, not selected from the test answer options, questions to the system, etc.), prototyping, constant iteration during natural language text recognition and processing, and maximum reliability

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Besharati Moghaddam, Fatemeh, Angel J. Lopez, Stijn De Vuyst, and Sidharta Gautama. "Natural Language Processing in Knowledge-Based Support for Operator Assistance." Applied Sciences 14, no. 7 (2024): 2766. http://dx.doi.org/10.3390/app14072766.

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Manufacturing industry faces increasing complexity in the performance of assembly tasks due to escalating demand for complex products with a greater number of variations. Operators require robust assistance systems to enhance productivity, efficiency, and safety. However, existing support services often fall short when operators encounter unstructured open questions and incomplete sentences due to primarily relying on procedural digital work instructions. This draws attention to the need for practical application of natural language processing (NLP) techniques. This study addresses these chall

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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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Thakkar, Krishna Yatin, and Nimit Jagdishbhai. "Exploring the capabilities and limitations of GPT and Chat GPT in natural language processing." Journal of Management Research and Analysis 10, no. 1 (2023): 18–20. http://dx.doi.org/10.18231/j.jmra.2023.004.

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Natural Language Processing (NLP) has seen tremendous advancements with the development of Generative Pretrained Transformer (GPT) models and their conversational variant, ChatGPT. These language models have been shown to generate contextually appropriate and coherent responses to natural language prompts, making them highly useful for various NLP applications. However, there are still limitations to their performance and understanding these limitations is crucial for their effective utilization. This paper presents a comprehensive analysis of the capabilities and limitations of GPT and ChatGP

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Vinod Singh, Vedansh, and Yukti Gupta. "ADVANCED RESUME PARSER USING NLP." International Journal of Engineering Applied Sciences and Technology 10, no. 01 (2025): 96–103. https://doi.org/10.33564/ijeast.2025.v10i01.014.

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This project focuses on developing an advanced resume parser using Natural Language Processing (NLP) techniques to automate the extraction of structured data from unstructured resumes. By applying methods such as Named Entity Recognition and keyword extraction, the system accurately identifies key sections like personal details, skills, education, and experience. The goal is to streamline recruitment processes and enhance hiring efficiency through intelligent data processing

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Akundi, Aditya, Joshua Ontiveros, and Sergio Luna. "Text-to-Model Transformation: Natural Language-Based Model Generation Framework." Systems 12, no. 9 (2024): 369. http://dx.doi.org/10.3390/systems12090369.

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System modeling language (SysML) diagrams generated manually by system modelers can sometimes be prone to errors, which are time-consuming and introduce subjectivity. Natural language processing (NLP) techniques and tools to create SysML diagrams can aid in improving software and systems design processes. Though NLP effectively extracts and analyzes raw text data, such as text-based requirement documents, to assist in design specification, natural language, inherent complexity, and variability pose challenges in accurately interpreting the data. In this paper, we explore the integration of NLP

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Sorupaa J, Jana, Anto Nivedha J, Arsha R, and Muthulakshmi K. "YouTube Comment Sentiment Classification System." March 2024 6, no. 1 (2024): 90–104. http://dx.doi.org/10.36548/jaicn.2024.1.007.

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With more than 2 billion viewers per month, YouTube is the most widely used video-sharing website worldwide. On this website, users can watch, upload, and share videos covering a wide range of subjects. YouTube comments include facts, opinions, and responses to videos in addition to starting discussions. The number of YouTube comments makes it difficult to manually analyze them all. The study of reading, understanding, and creating text in human languages encompasses a broad range of methods and techniques under the umbrella of natural language processing or NLP. The primary goal of the resear

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Badawi, Afif. "THE EFFECTIVENESS OF NATURAL LANGUAGE PROCESSING (NLP) AS A PROCESSING SOLUTION AND SEMANTIC IMPROVEMENT." International Journal of Economic, Technology and Social Sciences (Injects) 2, no. 1 (2021): 36–44. http://dx.doi.org/10.53695/injects.v2i1.194.

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The purpose of writing this article is to discuss the function of Natural Language Processing (NLP) in semantic improvement. The writing method uses literature related to NLP on semantic processing and refinement. The discussion in this paper shows that natural language processing helps computers communicate with humans in their own language and makes scaling other language-related tasks easier and more systematic. Because NLP includes lexical/scanner analysis, syntactic/parser analysis, semantic/translator analysis and pragmatic/evaluator analysis. Each component is a sequence of interrelated

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Movva, Himadeep. "Natural Language Processing in UiPath Communications Mining in Healthcare: Applications and Impact." International Journal of Multidisciplinary Research and Growth Evaluation 6, no. 3 (2025): 1204–8. https://doi.org/10.54660/.ijmrge.2025.6.3.1204-1208.

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Natural language processing in healthcare helps extract insights from unstructured data and eliminates manual work overload in analyzing, summarizing, and interpreting unstructured data. NLP recognizes characters in a document and comprehends what they mean. It can accurately segment the details and format the data into the Electronic Health Record (EHR) systems. This feature lets hospitals achieve improved clinical documentation, better patient care, efficient analysis of medical documents, and automation of repetitive administrative tasks. By integrating NLP with Robotic Process Automation (

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Verma, Praveen Kumar, and Dr Abhay Bhatia. "Personalized Care Through Sentiment Analysis and Natural Language Processing." International Journal of Soft Computing and Engineering 14, no. 6 (2025): 5–11. https://doi.org/10.35940/ijsce.f3657.14060125.

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In hospitals and other healthcare organizations, understanding patient feedback helps to exceed in providing top-notch care. Sentiment analysis to enhance patient care is the way to know how patients feel about different service aspects, including processes, infrastructure, treatment, and healthcare professionals. Enhancing healthcare with sentiment analysis means removing human bias through consistent analysis, gaining real-time insights about patient satisfaction, and improving standards of care by incorporating patient feedback. In this paper, we will examine several facets of utilizing sen

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Thessen, Anne E., Hong Cui, and Dmitry Mozzherin. "Applications of Natural Language Processing in Biodiversity Science." Advances in Bioinformatics 2012 (May 22, 2012): 1–17. http://dx.doi.org/10.1155/2012/391574.

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Centuries of biological knowledge are contained in the massive body of scientific literature, written for human-readability but too big for any one person to consume. Large-scale mining of information from the literature is necessary if biology is to transform into a data-driven science. A computer can handle the volume but cannot make sense of the language. This paper reviews and discusses the use of natural language processing (NLP) and machine-learning algorithms to extract information from systematic literature. NLP algorithms have been used for decades, but require special development for

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Di Sorbo, Andrea, and Sebastiano Panichella. "Summary of the 1st Natural Language-based Software Engineering Workshop (NLBSE 2022)." ACM SIGSOFT Software Engineering Notes 48, no. 1 (2023): 101–4. http://dx.doi.org/10.1145/3573074.3573101.

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Natural language processing (NLP) refers to automatic computa- tional processing of human language, including both algorithms that take human-produced text as input and algorithms that pro- duce natural-looking text as outputs. There is a widespread and growing usage of NLP approaches to optimize many aspects of the development process of software systems. In particular, since natural language artifacts are used and reused during the software development lifecycle, the availability of natural language-based approaches and tools enabled the envisioning of methods for im- proving efficiency in s

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Okonta, Ebere Donatus, Francis Ogochukwu Okeke, Emeka Ebuz Mgbemena, et al. "An Intelligent Natural Language Processing (NLP) Workflow for Automated Smart Building Design." Buildings 15, no. 14 (2025): 2413. https://doi.org/10.3390/buildings15142413.

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The automation of smart building design processes remains a significant challenge, particularly in translating complex natural language requirements into structured design parameters within Computer-Aided Design (CAD) environments. Traditional design workflows rely heavily on manual input, which can be inefficient, error-prone, and time-consuming, limiting the integration of adaptive, real-time inputs. To address this issue, this study proposes an intelligent Natural Language Processing (NLP)-based workflow for automating the conversion of design briefs into CAD-readable parameters. This study

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AHMAD TARMIZI, WAN AINOL MURSYIDA BINTI, Asma Nadia Zanol Rashid, AREENA AQILAH MUHAMMAD SAPRI, and MANI YANGKATISAL. "Natural Language Processing (NLP) Application For Classifying and Managing Tacit Knowledge in Revolutionizing AI-Driven Library." Information Management and Business Review 16, no. 3(I)S (2024): 1094–110. http://dx.doi.org/10.22610/imbr.v16i3(i)s.3949.

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The rapid evolution of technology has transformed library systems, with Natural Language Processing (NLP) emerging as a pivotal tool for enhancing knowledge management. This study aims to examine how NLP can improve the classification and management of tacit knowledge within AI-driven libraries, addressing the challenge of handling large volumes of unstructured data. The objective is to explore how NLP can optimize the retrieval, organization, and access to tacit knowledge, thus enhancing decision-making processes in libraries. The research adopts a conceptual design, synthesizing existing lit

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Sayeed Mohammed Sami Uddin, Md Abdullah Khan, Mohammed Rehan Ali, and Dr.Ahad Afroz. "WhatsApp Group Chat Analysis Using Natural Language Processing (NLP)." International Journal of Information Technology and Computer Engineering 13, no. 2s (2025): 468–76. https://doi.org/10.62647/ijitce2025v13i2spp468-476.

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In the digital communication era, WhatsApp hasemerged as one of the most widely usedmessaging platforms worldwide. With theexponential growth of data shared through groupchats, analyzing this unstructured data usingadvanced Natural Language Processing (NLP)techniques has become essential forunderstanding user behavior, communicationpatterns, and group dynamics. This studyintroduces an in-depth framework for WhatsAppgroup chat analysis by leveraging NLP andmachine learning to extract meaningful insightsfrom exported chat logs.The proposed system focuses on several keyobjectives: identifying the

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Yongjun Li, Xiongfei Li,. "Deep Learning and Natural Language Processing Technology Based Display and Analysis of Modern Artwork." Journal of Electrical Systems 20, no. 3s (2024): 1636–46. http://dx.doi.org/10.52783/jes.1704.

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The modern artwork analysis display system, empowered by natural language processing (NLP) technology, revolutionizes the way audiences interact with and understand art. By integrating NLP algorithms, this system offers a dynamic and user-friendly platform for analyzing and displaying artwork. Utilizing NLP, visitors can engage in interactive conversations with the system, asking questions or making inquiries about the artwork on display. The system processes these inquiries, extracting relevant information from curated databases and scholarly sources to provide insightful and context-rich res

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Kim, Yeji, Chanyoung Song, Gyuseon Song, Sol Bi Kim, Hyun-Wook Han, and Inbo Han. "Using Natural Language Processing to Identify Low Back Pain in Imaging Reports." Applied Sciences 12, no. 24 (2022): 12521. http://dx.doi.org/10.3390/app122412521.

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A natural language processing (NLP) pipeline was developed to identify lumbar spine imaging findings associated with low back pain (LBP) in X-radiation (X-ray), computed tomography (CT), and magnetic resonance imaging (MRI) reports. A total of 18,640 report datasets were randomly sampled (stratified by imaging modality) to obtain a balanced sample of 300 X-ray, 300 CT, and 300 MRI reports. A total of 23 radiologic findings potentially related to LBP were defined, and their presence was extracted from radiologic reports. In developing NLP pipelines, section and sentence segmentation from the ra

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Panichella, Sebastiano, and Andrea Di Sorbo. "Summary of the 2nd Natural Language-based Software Engineering Workshop (NLBSE 2023)." ACM SIGSOFT Software Engineering Notes 48, no. 4 (2023): 60–63. http://dx.doi.org/10.1145/3617946.3617957.

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Natural language processing (NLP) involves the automated anal- ysis and manipulation of human language. This includes algo- rithms that can analyze text created by humans and algorithms that can generate text that appears natural. Nowadays, NLP methods are becoming increasingly prevalent to enhance various aspects of software development. Indeed, throughout the software development lifecycle, numerous natural language artifacts are produced. Therefore, the existence of NLP-based approaches and tools has opened up possibilities for improving the e ectiveness and productivity of software enginee

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BELOKI, ZUHAITZ, XABIER ARTOLA, and AITOR SOROA. "A scalable architecture for data-intensive natural language processing." Natural Language Engineering 23, no. 5 (2017): 709–31. http://dx.doi.org/10.1017/s1351324917000092.

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AbstractComputational power needs have greatly increased during the last years, and this is also the case in the Natural Language Processing (NLP) area, where thousands of documents must be processed, i.e., linguistically analyzed, in a reasonable time frame. These computing needs have implied a radical change in the computing architectures and big-scale text processing techniques used in NLP. In this paper, we present a scalable architecture for distributed language processing. The architecture uses Storm to combine diverse NLP modules into a processing chain, which carries out the linguistic

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Penmetsa, G., S. Pei, B. Sauer, et al. "POS0262 IDENTIFYING EROSIVE DISEASE FROM RADIOLOGY REPORTS OF VETERANS WITH INFLAMMATORY ARTHRITIS USING NATURAL LANGUAGE PROCESSING." Annals of the Rheumatic Diseases 80, Suppl 1 (2021): 353.2–354. http://dx.doi.org/10.1136/annrheumdis-2021-eular.1794.

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Background:The presence of erosive disease influences diagnosis, management, and prognosis in inflammatory arthritis (IA).Research of IA in large datasets is limited by a lack of methods for identifying erosions.Objectives:To develop methods for identifying articular erosions in radiology reports from veterans with IA.Methods:Included veterans had ≥2 ICD codes for ankylosing spondylitis (AS), psoriatic arthritis (PsA), or rheumatoid arthritis (RA) between 2005- 2019, in Veterans Affairs Corporate Data Warehouse. Chart review & annotation of radiology notes produced the reference standard,

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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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Anjusha Pimpalshende. "Voice Based Answer Evaluation System for Physically Disabled using Natural Language Processing." Journal of Information Systems Engineering and Management 10, no. 30s (2025): 138–52. https://doi.org/10.52783/jisem.v10i30s.4784.

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The proposed work involves the selection of a subject and evaluation of student responses via a voice-based answer evaluation system that utilizes Natural Language Processing (NLP). This system aims to assist physically disabled individuals, who find it challenging to write their answers by hand. Traditional evaluation methods may become time-consuming, biased, and inconsistent in grading. The approach processes spoken answers, converts the voice signal to text, and finds the relevance of these text answers, according to certain criteria based on a predefined marking scheme. Using NLP techniqu

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Shaik, Nazeer. "The Nexus of AI and Vector Databases: Revolutionizing NLP with LLMs." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 06 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem35419.

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Vector databases play a critical role in the efficiency and functionality of large language models (LLMs), providing scalable and efficient storage and retrieval of high-dimensional vectors. This paper explores the significance of vector databases in the context of LLMs, highlighting their role in information retrieval, similarity search, training, and adaptation processes. Despite the challenges posed by high-dimensional data, vector databases offer invaluable benefits in enhancing the capabilities of LLMs and driving advancements in natural language processing (NLP). Future research and deve

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Das, Anshul, Prachi Goel, and Apurva Jain. "Natural Language Processing Based Classification of Publication Data." International Journal for Research in Applied Science and Engineering Technology 11, no. 12 (2023): 2044–46. http://dx.doi.org/10.22214/ijraset.2023.57762.

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Abstract: In the ever-expanding landscape of scholarly publications, the need for efficient and accurate methods of classifying and organizing vast amounts of information has become imperative. This research explores the application of Natural Language Processing (NLP) techniques to enhance the classification of publication data. By leveraging advanced linguistic and machine learning approaches, we aim to automate and optimize the categorization of diverse publications, thereby facilitating streamlined access to relevant knowledge.The proposed methodology involves the extraction of key feature

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