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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 participate in the NLP pipeline processes, explicitly manipulate the parameters of each step, and explore the result visually based on user preferences. The visible NLP (VNLP) pipeline design is then applied to a text similarity application to demonstrate the utility and advantages of a visible and transparent NLP pipeline in supporting users to understand and justify both the process and results. We also report feedback on our framework from a modern languages expert.
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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 engineering in NLP.
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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, predicting market trends, and breaking down linguistic barriers in the age of globalization. This paper sheds light on the evolution of NLP techniques in business analytics, their applications, and the inherent challenges and opportunities they present.
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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 NLP and NPD and conducted a literature review to map out how far NLP has been utilized in NPD processes. We found that in this era of Big Data, current NLP studies most often have the goal to process text data from online reviews on e-commerce and from social media. By using NLP, large amounts of data can produce valuable Voice of Customer (VOC) information for product development. We also found that NLP technology also has been utilized in other NPD processes that do not involve VOC, such as the design stage, document processing, and extraction of requirements in the NPD process.
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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 syntactical search rather than a semantic search. To overcome such limitations, this paper explores the use of well-known natural language processing (NLP) techniques to support a semantic search and identifies where specific NLP techniques can be employed and what their primary benefits are, thus enhancing the opportunities to further improve the research process. The proposed NLP methods were tested through different workflows on different datasets and each workflow was designed to exploit latent relationships within the data to refine the keywords. The results of these tests demonstrated an improvement in the identified literature when compared to the literature extracted from the end-user-given keywords. For example, one of the defined workflows reduced the number of search results by two orders of magnitude but contained a larger percentage of pertinent results.
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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 includes content determination (identifying relevant information), text planning (organizing information), sentence planning (constructing grammatically correct sentences), and surface realization (generating the final text). NLG is crucial for applications like automated report generation and chat bots, where coherent and contextually appropriate responses are essential. The synergy between NLU and NLG underpins many NLP applications. In machine translation, NLU interprets the source text, while NLG generates the translated text. In question-answering systems, NLU processes the query, and NLG formulates the response. Deep learning advancements have significantly enhanced both NLU and NLG, enabling more sophisticated and human-like interactions. This paper explores the components and processes of NLU and NLG, offering a comprehensive understanding of their mechanisms and the advancements driving modern NLP systems.
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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 system may also more effectively understand and produce code that is compatible with a wider range of programming languages and paradigms. This research clarifies the revolutionary potential of NLP and ML-driven code creation and highlights its consequences for software development efficiency, accessibility, and innovation through an extensive assessment of current developments and case examples.
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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 YouTube’s API, data cleaning, preprocessing (tokenization, sentence segmentation, linguistic normalization), sentiment analysis using Python, and topic modeling. This study serves as a comprehensive guide for integrating NLP into nursing research, supplemented with digital content demonstrating each step. For successful implementation, nursing researchers must grasp the fundamental concepts and processes of NLP. The potential of NLP in nursing is significant, particularly in utilizing unstructured textual data from nursing documentation and social media. Its benefits include streamlining nursing documentation, enhancing patient communication, and improving data analysis.
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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 field of business management and marketing prediction, this survey article reinterprets the evolution of NLP research as the intersection of three overlapping curves-namely Syntactics, Semantics, and Pragmatics Curves- which will eventually lead NLP research to evolve into natural language understanding.
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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 recognition, DS can efficiently scrap textual data to identify patterns, trends and sentiments hidden behind it. Additionally, machine learning algorithms take advantage of the automated nature of text insights creation and employ it for the decision making tasks. After the critical assessment of the text mining and NLP literature review along with the case studies, this paper reveals the opportunities of data mining and NLP systems in emphasizing the range of industries where decisions are required. Moreover, it addresses the challenges and future research agenda for the future use of these approaches into frame decision-making processes.</em>
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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 candidates with that qualification. NLP algorithms can recognize relevant keywords, synonyms, and context, even if the phrasing varies across resumes. This means that someone who writes "managed team projects" or "project lead" can be identified as having similar experience. The system uses advanced NLP models, such as BERT or GPT, to capture subtle language details and improve accuracy. As it processes more resumes, the system "learns" and becomes better at identifying relevant skills and qualifications. Customization allows it to adapt to different industries by prioritizing specific keywords and competencies. This adaptability makes the system valuable across fields like technology, finance, and healthcare. By focusing on qualifications objectively, it helps reduce bias in resume review. Recruiters save time and can focus on candidates who meet job requirements more closely. The system accelerates hiring, improves candidate-job matching, and supports data-driven decisions. Keywords Resume Analysis, Natural Language Processing (NLP), Recruitment Automation, Text Analysis, Skill Extraction, Information Retrieval, Automated Resume Parsing, Unstructured Data Processing.
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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 labeling clinical notes from the EHR containing the key word “venting” near the time of the procedure. Documentation of ACP, PC, and hospice referral was identified by NLP using a validated key term library. The sensitivity and specificity of the NLP method was determined by comparing outcome identification to manual chart abstraction performed by two clinicians. All NLP code was written in the open-source programming language Python. Results: NLP was performed for 75,626 documents. Among 305 cancer patients who underwent gastrostomy, 75 (24.6%) were classified by NLP as having a palliative indication for the procedure compared to 72 patients (23.6%) classified by human coders. Manual chart abstraction took > 2,600 times longer than NLP (28 hrs vs. 38 seconds). NLP identified the correct patients with high precision (0.92) and recall (0.96). ACP was documented during the index admission for 89.3% of patients. PC was documented for 85.7% and hospice referral was documented for 64.3% of these patients with advanced cancer during the index hospitalization. NLP identified ACP, PC and hospice referral with high precision (0.88-1.0) and recall (0.92-1.0) compared to human coders. Median survival was 37 days following gastrostomy tube procedure. Conclusions: NLP can greatly speed the assessment of established palliative quality metrics with an accuracy approaching that of human coders. These methods offer opportunities for facilitate quality improvement in palliative care for patients with advanced cancer.
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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. The article allows us to trace how the idea of natural language text has changed during the development of computer analysis methods, as well as to understand what text is in the mirror of natural language processing, what is really the subject of natural language processing research and what cannot be seen through the eyes of a simple researcher who does not use NLP methods.
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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 their daily five-times-prayer practice, and Arabic is one of six official languages used by more than 422 million people in the Arab world, according to UNESCO. Dialectal Arabic is the slang language spoken informally in everyday life and varies from country to country; Classical Arabic is a reflection of the language spoken by the Arabs more than fourteen centuries ago. Modern Standard Arabic is an evolving variety of Arabic that borrows and innovates regularly to meet the changing needs of its speakers. Complexity is added to the Arabic language by the fact that it encompasses not one but three different varieties of spoken language: classical, contemporary, and colloquial. However, Arabic language processing on computers remains difficult for a variety of reasons. Because of the extensive inflection and derivational processes that occur in Arabic, a single lemma may be utilized to produce several words with distinct meanings.
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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. Following the guidance of systematic review, the mapping study is directed by five research questions, cutting across five aspects of NLP4RE research, concerning the state of the literature, the state of empirical research, the research focus, the state of tool development, and the usage of NLP technologies. Our main results are as follows: (i) we identify a total of 404 primary studies relevant to NLP4RE, which were published over the past 36 years and from 170 different venues; (ii) most of these studies (67.08%) are solution proposals, assessed by a laboratory experiment or an example application, while only a small percentage (7%) are assessed in industrial settings; (iii) a large proportion of the studies (42.70%) focus on the requirements analysis phase, with quality defect detection as their central task and requirements specification as their commonly processed document type; (iv) 130 NLP4RE tools (i.e., RE specific NLP tools) are extracted from these studies, but only 17 of them (13.08%) are available for download; (v) 231 different NLP technologies are also identified, comprising 140 NLP techniques, 66 NLP tools, and 25 NLP resources, but most of them—particularly those novel NLP techniques and specialized tools—are used infrequently; by contrast, commonly used NLP technologies are traditional analysis techniques (e.g., POS tagging and tokenization), general-purpose tools (e.g., Stanford CoreNLP and GATE) and generic language lexicons (WordNet and British National Corpus). The mapping study not only provides a collection of the literature in NLP4RE but also, more importantly, establishes a structure to frame the existing literature through categorization, synthesis and conceptualization of the main theoretical concepts and relationships that encompass both RE and NLP aspects. Our work thus produces a conceptual framework of NLP4RE. The framework is used to identify research gaps and directions, highlight technology transfer needs, and encourage more synergies between the RE community, the NLP one, and the software and systems practitioners. Our results can be used as a starting point to frame future studies according to a well-defined terminology and can be expanded as new technologies and novel solutions emerge.
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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 approaches developed so far, categorizing them by type, i.e., theoretical work and those implemented on classical or quantum hardware; by task, i.e., general purpose such as syntax-semantic representation or specific NLP tasks, like sentiment analysis or question answering; and by the resource used in the evaluation phase, i.e., whether a benchmark dataset or a custom one has been used. The advantages offered by QNLP are discussed, both in terms of performance and methodology, and some considerations about the possible usage QNLP approaches in the place of state-of-the-art deep learning-based ones are given.
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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 overview and commentary on key existing and developing tools that use NLP or machine learning techniques for archives. 4) This review and analysis will inform a discussion of functional requirements and workflow considerations for NLP and machine learning tools for archival processing. Findings Applications for processing e-mail have received the most attention so far, although most initiatives have been experimental or project based. It now seems feasible to branch out to develop more generalized tools for born-digital, unstructured records. Effective NLP and machine learning tools for archival processing should be usable, interoperable, flexible, iterative and configurable. Originality/value Most implementations of NLP for archives have been experimental or project based. The main exception that has moved into production is ePADD, which includes robust NLP features through its named entity recognition module. This paper takes a broader view, assessing the prospects and possible directions for integrating NLP tools and techniques into archival workflows.
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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 (IDP) and fine-tuned transformer-based models (BERT and RoBERTa) to classify personality traits from textual data. Our results demonstrate moderate to high accuracy in personality prediction, with NLP models effectively identifying personality signals in text in line with previous studies. Our findings reveal theory-coherent patterns in language use associated with different personality traits, while highlighting important biases in the MBTI dataset that yielded less robust results. The study underscores the potential of NLP in enhancing personality psychology and emphasizes the need for further interdisciplinary research to fully realize the capabilities of these transparent technologies.
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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 trading, considering investors' preferences and time constraints. In the current manual processes, investors often spend substantial time reading articles and processing information before making decisions. This approach is inefficient, consuming excessive time and energy, thereby reducing the precious time that should be saved for personal relationships. Moreover, suboptimal decision-making could be made due to frequently gathered of inaccurate information. This research aims to discover the impact of Natural Language Processing integration with the stock prediction model on the financial market and evaluates the acceptance of the public towards the employment of NLP tools in their investment process for inclusive innovation. The evaluation will examine 4 different perspectives which are the factors that drive them to invest in the stock market, assessing the model's effectiveness, and the user experience respectively. This study utilized a mixed-method approach, which consists of quantitative and qualitative surveys. The respondents evidence the results and are being analyzed using SmartPLS, a statistical tool. Most respondents indicated a willingness to utilise NLP provided it effectively helps them achieve their financial goals and fosters positive experiences. With the implementation of NLP, respondents anticipated that NLP would significantly reduce the time for investment research and analysis. Consequently, investors could achieve higher profits while dedicating more time to their families. Besides, by identifying the trends through the textual data, NLP is expected to enhance the accuracy of stock prediction results. Thus, the potential opportunities can be uncovered with reduced downside risks.
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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 paper will review the NLP methods and resources that have been performed on the Malagasy language only. Recently, work on the automatic processing of the Malagasy language occurs even if the language is part of the group of under-endowed languages. This will allow researchers who are interested in the Malagasy language to update themselves in terms of the work already carried out, and the challenges that may be envisaged in future.
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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 documents, and electronic health records. These applications facilitate more effective drug discovery, preclinical testing, clinical trials, pharmacovigilance, and regulatory compliance. Furthermore, NLP tools are helping researchers identify novel drug targets, optimize clinical trial designs, improve patient recruitment, and monitor post-market safety.(1).
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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 efficiency of learning processes. The purpose of the article is to study and analyse various methods of natural language processing, and the concept of NLP, and to consider common problems and prospects for developing a software product for processing Ukrainian-language text in online courses that support intelligent learning systems based on it. The research methods are the main methodological approaches and technological tools for analysing natural language texts in intelligent educational systems and developing a system for supporting NLP (Natural Language Processing) technology in the linguistic analysis of texts in Ukrainian. Such methods include, in particular: systemic and comparative analyses to identify the features of intelligence and information (with elements of intellectualisation) systems; the method of expert evaluation, which involves the study of literary sources and information resources, interviews and surveys of experts, as well as the processes of developing and testing intelligent and information systems. The novelty of the study is the analysis of modern technologies for the development of online educational process support systems through the organisation of processes of perception of information provided by students in natural language, the results of which can be used in the development of their software product to support the educational process in Ukrainian, ensuring the improvement of learning efficiency through the use of NLP technology in the process of studying the relevant academic content. Conclusions. The paper analyses modern NLP methods. The analysis has led to the selection of tokenisation, normalisation, stemming and lemmatisation methods for use in intelligent learning systems in the linguistic analysis of the so-called “free” communication in the natural (Ukrainian) language of students in the process of studying the educational content of online courses. During the tokenisation of Ukrainian-language texts, we solved such problems as eliminating so-called “merged” tokens, correcting spelling mistakes, identifying common prefixes in compound words and their impact on the semantics of the corresponding lexemes, identifying common prefixes in abbreviations, and bringing words to their normal form. Lemmatisation is especially important for the Ukrainian language (with its large number of cases of nouns, adjectives, word forms, etc.) and it requires the use of specially compiled dictionaries of the subject area under consideration. In these dictionaries, word forms are presented in the forms of lemmas (i.e., nouns are presented in the nominative case).
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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 analysis, and knowledge graphs, NLP systems process regulatory documents with substantially higher accuracy than conventional review methods. The implementation architecture integrates data acquisition, analytical processing, and business integration layers to create end-to-end compliance traceability. Real-world implementations demonstrate significant improvements in processing time, accuracy, and cost savings. Despite challenges including regulatory ambiguity and cross-jurisdictional variations, the strategic implementation of NLP solutions with human-in-the-loop frameworks and ethical considerations offers transformative potential for regulatory compliance, reducing operational risks while strengthening financial institutions' ability to meet global reporting obligations in an increasingly complex regulatory landscape.
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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 challenges by introducing a domain-specific dataset tailored to assembly tasks, capturing unique language patterns and linguistic characteristics. We explore strategies to process declarative and imperative sentences, including incomplete ones, effectively. Thorough evaluation of three pre-trained NLP libraries—NLTK, SPACY, and Stanford—is performed to assess their effectiveness in handling assembly-related concepts and ability to address the domain’s distinctive challenges. Our findings demonstrate the efficient performance of these open-source NLP libraries in accurately handling assembly-related concepts. By providing valuable insights, our research contributes to developing intelligent operator assistance systems, bridging the gap between NLP techniques and the assembly domain within manufacturing industry.
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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. The review shows that the current literature comprises three levels of contribution: automation, extension, and transformation. The most-used NLP techniques reported in government-related research are sentiment analysis, machine learning, deep learning, classification, data extraction, data mining, topic modelling, opinion mining, chatbots, and question answering. Data classification, management, and decision-making are the most frequently reported reasons for using NLP. The salient research topics being discussed in the literature can be grouped into four categories: (1) governance and policy, (2) citizens and public opinion, (3) medical and healthcare, and (4) economy and environment. Future research directions should focus on (1) the potential of chatbots, (2) NLP applications in the post-pandemic era, and (3) empirical research for government work.
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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.
Pełny tekst źródłaStreszczenie:
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 efficiency of learning processes. <strong>The purpose of the article</strong> is to study and analyse various methods of natural language processing, and the concept of NLP, and to consider common problems and prospects for developing a software product for processing Ukrainian-language text in online courses that support intelligent learning systems based on it. <strong>The research methods</strong> are the main methodological approaches and technological tools for analysing natural language texts in intelligent educational systems and developing a system for supporting NLP (Natural Language Processing) technology in the linguistic analysis of texts in Ukrainian. Such methods include, in particular: systemic and comparative analyses to identify the features of intelligence and information (with elements of intellectualisation) systems; the method of expert evaluation, which involves the study of literary sources and information resources, interviews and surveys of experts, as well as the processes of developing and testing intelligent and information systems. <strong>The novelty of the study</strong> is the analysis of modern technologies for the development of online educational process support systems through the organisation of processes of perception of information provided by students in natural language, the results of which can be used in the development of their software product to support the educational process in Ukrainian, ensuring the improvement of learning efficiency through the use of NLP technology in the process of studying the relevant academic content. <strong>Conclusions.</strong> The paper analyses modern NLP methods. The analysis has led to the selection of tokenisation, normalisation, stemming and lemmatisation methods for use in intelligent learning systems in the linguistic analysis of the so-called “free” communication in the natural (Ukrainian) language of students in the process of studying the educational content of online courses. During the tokenisation of Ukrainian-language texts, we solved such problems as eliminating so-called “merged” tokens, correcting spelling mistakes, identifying common prefixes in compound words and their impact on the semantics of the corresponding lexemes, identifying common prefixes in abbreviations, and bringing words to their normal form. Lemmatisation is especially important for the Ukrainian language (with its large number of cases of nouns, adjectives, word forms, etc.) and it requires the use of specially compiled dictionaries of the subject area under consideration. In these dictionaries, word forms are presented in the forms of lemmas (i.e., nouns are presented in the nominative case).
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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 ChatGPT, covering their architecture, training processes, and evaluation metrics. The study also evaluates the performance of these models on various NLP tasks, including language translation, question-answering, and text summarization. The results reveal that while these models excel in certain tasks, they still face challenges in understanding context, generating diverse responses, and handling rare or out-of-domain inputs. The study concludes by discussing potential solutions and future research directions for improving the performance of GPT and ChatGPT in NLP applications.
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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 with SysML to automate the generation of system models from input textual requirements. We propose a model generation framework leveraging Python and the spaCy NLP library to process text input and generate class/block definition diagrams using PlantUML for visual representation. The intent of this framework is to aid in reducing the manual effort in creating SysML v1.6 diagrams—class/block definition diagrams in this case. We evaluate the effectiveness of the framework using precision and recall measures. The contribution of this paper to the systems modeling domain is two-fold. First, a review and analysis of natural language processing techniques for the automated generation of SysML diagrams are provided. Second, a framework to automatically extract textual relationships tailored for generating a class diagram/block diagram that contains the classes/blocks, their relationships, methods, and attributes is presented.
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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 research is to find and analyze YouTube comments, which, when used with natural language processing algorithms, might be beneficial for the channels' continued development. One of the NLP methods used for this research was tokenization, which is used to break down text into individual words or tokens. Stemming and lemmatization are used to reduce the root words and normalize the variation. Categorization is performed by identifying the named entities, such as people, organizations, and locations. The machine translation was used to convert the comments from one language to another.
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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 processes and requires a knowledge base to process a language. Lexical analysis requires knowledge of vocabulary (lexicon) to understand word formation. Syntax analysis requires knowledge of grammar rules (grammar) to understand the structure of a sentence. Semantic analysis requires knowledge of the meaning and meaning of words to understand the relationship between words and the meaning of a sentence. Pragmatic analysis requires knowledge of a concept to understand the relationship between language and the context in which it is used.
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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 (RPA), healthcare organizations can automate manual operations and improve the efficiency of processes that entail extracting and analyzing data from documents. This research paper presents how RPA and NLP can be integrated using UiPath Communications Mining. This study also mentions how to create data sets and analyze the end-to-end process to analyze the data in UiPath Communications Mining.
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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 sentiment analysis for patient happiness, such as the various forms of sentiment analysis, its applications in healthcare, and its precise methodology. This work intends to guide algorithm selection and progress NLP research by adding to the continuing conversation on advancing sentiment analysis in the context of big data and computational linguistics. These results highlight the adaptability of NLP methods and their potential to enhance patient outcomes, research, and healthcare delivery.
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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 application in the biological realm due to the special nature of the language. Many tools exist for biological information extraction (cellular processes, taxonomic names, and morphological characters), but none have been applied life wide and most still require testing and development. Progress has been made in developing algorithms for automated annotation of taxonomic text, identification of taxonomic names in text, and extraction of morphological character information from taxonomic descriptions. This manuscript will briefly discuss the key steps in applying information extraction tools to enhance biodiversity science.
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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 software engineers, processes, and products. The research community has been discussing these approaches in the 1st edition of the Natural Language-Based Software Engineer- ing Workshop (NLBSE), collocated with ICSE (the International Conference on Software Engineering) in 2022. This event brought together researchers and industrial practitioners from NLP and the software engineering community to share experiences, pro- vide directions for future research, and encourage the usage of NLP techniques and tools for addressing software engineering- speci c challenges. In this paper, we present a summary of the 1st edition of the workshop, which comprised ve full papers, four short/position papers, ve tool competition/demonstration pa- pers, one keynote (\Deep Learning & Software Engineering: Past, Present and Future"by Denys Poshyvanyk), followed by extensive discussion among NLBSE participants. More details can be found at https://nlbse2022.github.io/index.html
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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 literature and theoretical models, including Information Processing Theory and Constructivist Theory, to propose a framework that integrates NLP with traditional knowledge management practices. Methodologies include a thorough review of recent advancements in NLP technologies and their applications within knowledge management systems. The study’s findings demonstrate that NLP significantly improves the accuracy and efficiency of knowledge retrieval by automating the processing of natural language data. This allows better access to tacit knowledge, supporting more informed decision-making. The outcomes of the study are twofold: it enhances existing knowledge management frameworks theoretically, and it provides practical insights for libraries to leverage NLP for greater operational efficiency and improved user experience. The study also underscores the need for future research on the real-world application of NLP and its ethical implications, such as data privacy and algorithmic bias.
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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 most active andinactive participants in a group, analyzingmessage frequency over time, understandingsentiment trends, and detecting frequentlydiscussed topics. The input to the system is theraw text format of WhatsApp chats exported byusers. This data is then preprocessed usingvarious NLP methods including tokenization,lemmatization, removal of stop words, and emojihandling. Once cleaned, the dataset is subjectedto analytical processes such as frequencyanalysis, word clouds, temporal message densityplots, and sentiment classification using librarieslike NLTK, TextBlob, and VADER.In addition to basic chat statistics (such as thenumber of messages, media files, links, anddeleted messages), our system performs sentimentanalysis to gauge the emotional tone ofconversations over time. This is particularlyuseful in educational, corporate, or socialresearch settings where communication tone andbehavioral insights are important. Moreover,topic modeling techniques such as LatentDirichlet Allocation (LDA) are used to extracthidden themes in conversations, enabling a moregranular understanding of group discussions.The system also introduces a visual dashboardthat presents key findings in the form of graphs,heatmaps, and pie charts. For example, daily orweekly activity trends are visualized to show peakinteraction times, while pie charts display theproportional contribution of each participant.Deleted message tracking helps identify possiblesensitive or hidden content trends, which may beimportant in digital forensics or behaviormonitoring.Through real-world datasets collected frommultiple anonymous WhatsApp groups(educational, work-related, and casual), theanalysis demonstrated consistent accuracy indetecting message patterns, identifying leadingcontributors, and mapping emotional tonechanges over time. These insights are not onlybeneficial for sociologists and digitalcommunication researchers but also applicable inbusiness, education, and legal domains foranalyzing team dynamics, compliance, andengagement.This research contributes to the field of textanalytics by demonstrating how powerful insightscan be extracted from personal and group chatdata using NLP. It also opens doors for futureenhancements such as real-time chat analysis,multilingual sentiment evaluation, spamdetection, and integration with advanced AImodels like transformers and LLMs for deeperconversational understanding.In conclusion, this WhatsApp Group Analysissystem transforms static chat logs into dynamicand interactive interpretations of digitalconversations. It bridges the gap between rawdata and decision-making, providing a tool forboth academic exploration and practicalapplications in the modern communicationlandscape.
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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 responses. Additionally, NLP algorithms can analyze textual descriptions, artist statements, and critical reviews to offer nuanced interpretations and historical context for each artwork. This paper presents the design and implementation of an innovative modern artwork analysis and display system, leveraging deep learning and natural language processing (NLP) technology, integrated with Multi-Feature Extraction Fuzzy Classification (MFEFC). The system offers a comprehensive platform for analyzing and presenting modern artworks, enhancing user engagement and understanding. Deep learning algorithms are employed to extract high-level features from visual artworks, allowing for automatic recognition of artistic styles, genres, and themes. Concurrently, NLP techniques process textual descriptions, artist biographies, and critical reviews to provide contextual information and interpretative insights. The integration of MFEFC enables precise classification of artworks based on multiple features extracted from both visual and textual sources, facilitating accurate analysis and categorization. Simulation of the NLP techniques demonstrated an average precision of 90% in extracting relevant contextual information from textual descriptions and artist biographies. Furthermore, MFEFC achieved a classification accuracy of 88% in categorizing artworks based on combined visual and textual features.
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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 radiology reports was performed using a rule-based method, including regular expression with negation detection. Datasets were randomly split into 80% for development and 20% for testing to evaluate the model’s extraction performance. The performance of the NLP pipeline was evaluated by using recall, precision, accuracy, and the F1 score. In evaluating NLP model performances, four parameters—recall, precision, accuracy, and F1 score—were greater than 0.9 for all 23 radiologic findings. These four scores were 1.0 for 10 radiologic findings (listhesis, annular fissure, disc bulge, disc extrusion, disc protrusion, endplate edema or Type 1 Modic change, lateral recess stenosis, Schmorl’s node, osteophyte, and any stenosis). In the seven potentially clinically important radiologic findings, the F1 score ranged from 0.9882 to 1.0. In this study, a rule-based NLP system identifying 23 findings related to LBP from X-ray, CT, and MRI reports was developed, and it presented good performance in regards to the four scoring parameters.
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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 engineers, processes, and products. The second edition of the Natural Language-Based Software Engi- neering Workshop (NLBSE) took place in 2023 alongside the 45th International Conference on Software Engineering (ICSE 2023), where the research community engaged in discussions about these approaches. This event brought together researchers and practi- tioners from the elds of NLP and software engineering to ex- change experiences, establish future research directions, and pro- mote the adoption of NLP techniques and tools in tackling chal- lenges speci c to software engineering. In this paper, we present a summary of the 2nd edition of the workshop, which comprised three full papers, four short/position papers, ve tool competi- tion/demonstration papers, two keynote talks (\Automated Bug Management: Re ections & the Road Ahead" by David Lo and \Trends and Opportunities in the Application of Large Language Models: the Quest for Maximum E ect" by Albert Ziegler), fol- lowed by extensive discussion among NLBSE participants. More details can be found at https://nlbse2023.github.io/index. html
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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 analysis of documents. Scalability requires designing solutions that are able to run distributed programs in parallel and across large machine clusters. Using the architecture presented here, it is possible to integrate a set of third-party NLP modules into a unique processing chain which can be deployed onto a distributed environment, i.e., a cluster of machines, so allowing the language-processing modules run in parallel. No restrictions are placed a priori on the NLP modules apart of being able to consume and produce linguistic annotations following a given format. We show the feasibility of our approach by integrating two linguistic processing chains for English and Spanish. Moreover, we provide several scripts that allow building from scratch a whole distributed architecture that can be then easily installed and deployed onto a cluster of machines. The scripts and the NLP modules used in the paper are publicly available and distributed under free licenses. In the paper, we also describe a series of experiments carried out in the context of the NewsReader project with the goal of testing how the system behaves in different scenarios.
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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, & identified erosion terms that informed classification rule development. A rule-based natural language processing (NLP) model was created & revised in training snippets. The NLP method was validated in an independent reference sample of IA patients at the snippet & patient levelsStepDescriptionNumber & example1 Radiology notesa.Select note titles potentially relevant to IAa. 35,141 notes titlesb.Extract notes with titles potentially related to IAb. 2,926,113 radiology notes2 Possible meaningful termsa.Compile list of root terms that may indicate erosiona. 11 root terms (i.e. ero*, pencil*cup, irreg*)b.Query radiology notes for root term variationsb. 1178 variations (i.e. erosion, erotic, erode)c.Select possible meaningful termsc. 179 possible terms (i.e. erosion, erode)3 Annotationa.Extract snippets^ containing possible meaningful termsa.5000 snippets from radiology notesb.Classify snippets according to: 1) Meaningful term, 2) Relevance to joint, 3) Attribution to IA, 4) Affirmationb.4068 classifications with 1017 snippets (in rounds of 50-417 snippets for NLP training & testing)4 Rule developmenta.Identify meaningful terms representing erosiona. 6 terms (pencil * cup, erosion, erosive, etc.)b.Exclude erosive processes irrelevant to joint(s)b. 28 irrelevant processes (i.e. gastric erosion)c. Exclude articular erosive processes not attributed to IAc. 5 non-IA processes IA (i.e. infection)d. Classify as affirmed/negated (erosion present/absent)d. 83 affirmation/negation rules5 NLP trainingDesign & revise NLP model until accuracy ≥90%6 rounds, 817 snippets (AS 417, RA 200, PsA 200)6 NLP testingTest NLP model200 snippets (AS 100, RA 50, PsA 50)7 Pt classificationa. Develop rules for classifying pts with discordant snippetsa. 5 rules developed in 368 ptsb. Build reference sample (pts classified as erosive or non-erosive via chart review)b. 30 IA pts (10 AS, 10 RA, 10 PsA)8 NLP validationValidate NLP model in reference sample at snippet level149 snippets (29 AS, 76 RA, 44 PsA)9 Method validationValidate methods (NLP+pt classification) at pt level30 IA pts (reference sample)pt= patient. ^Snippets include text containing 30 words before & after meaningful termsResults:In 168,667 veterans with IA, the mean age was 63.1 & 90.3% were male. Method development involved radiology note & erosion term selection, rule development, NLP model building, & method validation. The NLP model accuracy was 94.6% at the snippet level & 90.0% at the patient level, for all IA patients.Accuracy of methods.Conclusion:The methods accurately identify erosions from radiology reports of veterans with IA. They may facilitate a broad range of research involving cohort identification & disease severity stratificationReferences:[1]Walsh JA, et al. J Rheumatol. 2020;47(1):42-49Disclosure of Interests:Gopi Penmetsa: None declared, Shaobo Pei: None declared, Brian Sauer Grant/research support from: I have been an investigator on research contracts supported by Abbvie., Jessica A. Walsh Consultant of: AbbVie, Amgen, Janssen, Lilly, Novartis, Pfizer, UCB, Grant/research support from: AbbVie, Merck, Pfizer, Bingjian Feng Grant/research support from: Bing-Jian Feng reports funding and sponsorship to his institution on his behalf from Pfizer Inc., Regeneron Genetics Center LLC, and Astra Zeneca (UK). The PERCH software, for which Bing-Jian Feng is the inventor, has been non-exclusively licensed to Ambry Genetics for clinical genetic testing services and research., Jodi Walker Shareholder of: Abbvie and mutual funds containing various pharmaceutical companies, Employee of: Abbvie, Kevin Douglas Shareholder of: employed by Abbvie, Employee of: employed by Abbvie, Jerry Clewell Shareholder of: Own Abbvie Shares and mutual funds that hold pharmaceutical and other health care stocks, Employee of: I am current Abbvie Inc employee and past employee of Eli Lilly co
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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-learning model development included text classification, named entity recognition, relation extraction and false positive removal. A subset of nearly 60,000 lung cancer cases were included from the CancerLinQ database, comprised of multiple source EHR systems. NLP models extracted EGFR status, stage, histology, radiation therapy, surgical resection and oral medications. Based on the results, cases were selected for additional manual curation, where curators confirmed findings of the NLP-processed data. Results: NLP methods improved cohort identification. Successfully returned cases using the NLP method ranged from 75.2% to 96.5% over more general case selection criteria based on limited structured data. For all cohorts combined, 84.2% of the cases sent out for NLP curation were returned with curated content (Table). Each cohort contained a range of NLP-derived elements for curators to further review. In comparison, more general case selection criteria yielded a total of 3,878 cases returned out of 41,186 lung cancer cases sent for curation, for a success rate of only 9.6%. Conclusions: NLP-driven case selection of six distinct, complex lung cohorts resulted in an order of magnitude improvement in eligibility over candidate selection using structured EHR data alone. This study demonstrates NLP-assisted approaches can significantly improve efficiency in curating unstructured health data. [Table: see text]
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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 techniques ensures maximum grading accuracy with minimum human interaction. The developed system seems to hold promise in accurate evaluation of responses, reduced bias, and greater accessibility for disabled persons.
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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 development in this area promise to further optimize the integration and performance of vector databases, fueling continued innovation in LLM applications. Keywords: Vector databases, Large language models (LLMs), Natural language processing (NLP), Information retrieval, Similarity search, Training, Adaptation, Scalability, Efficiency.
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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 features from textual content, such as abstracts, titles, and keywords, using state-of-the-art NLP algorithms. These features serve as input for a robust classification model that is trained on a diverse dataset of publications spanning various domains. The model's performance is fine-tuned through iterative processes, ensuring adaptability to the nuances and evolving trends within different research fields. Furthermore, we explore the integration of domain-specific ontologies and semantic analysis to enhance the precision and granularity of classification. This allows for a more nuanced understanding of the relationships between publications, enabling users to navigate through knowledge landscapes with increased contextual relevance.The study's significance lies in its potential to revolutionize the way researchers, academics, and professionals access and organize vast amounts of information. The proposed NLP-based classification system not only promises efficiency in information retrieval but also lays the groundwork for developing intelligent recommendation systems tailored to individual user preferences and research interests.Ultimately, this research contributes to the evolving field of information science by presenting a novel approach to publication data classification that aligns with the accelerating pace of information creation and dissemination in today's knowledge-driven society
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Banawa, Jemar Almaceda, and Mervin Jommel Tibay De Jesus. "Client Satisfaction Analysis for Delivery of Services with Natural Language Processing and Decision Support System." International Journal of Research and Innovation in Social Science IX, no. V (2025): 3733–45. https://doi.org/10.47772/ijriss.2025.905000283.
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Traditional client satisfaction surveys in state universities and government agencies often suffer from inefficiencies in data collection, analysis, and visualization. Manual processing leads to delays, inaccuracies, and limited actionable insights, hindering effective decision-making. This study aims to improve client satisfaction surveys by developing a web-based application that integrates Natural Language Processing (NLP) and Decision Support Systems (DSS). The goal is to automate the collection, analysis, and visualization of feedback, enhancing data-driven decision-making and service improvement. A developmental and descriptive research design was used, with data collected from university employees and clients involved in service delivery and surveys. Stratified sampling ensured diverse representation from faculty and students. The system was developed using Agile methodologies, allowing for iterative improvements based on user feedback. NLP was applied to analyze open-ended responses, while DSS was used to generate actionable insights. The system reduced survey processing delays by automating data analysis and visualization. NLP sentiment analysis improved the accuracy of open-ended feedback. Real-time insights were provided through interactive dashboards, aligning with the Anti-Red Tape Authority’s (ARTA) goal of improving government service efficiency. The web-based application effectively solved inefficiencies in traditional survey methods by automating key processes. Integrating NLP and DSS improved data accuracy, reduced delays, and enhanced service delivery in government institutions. State universities and government agencies should adopt this approach to enhance the efficiency and transparency of client satisfaction surveys, with further research exploring its application in other government processes.
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Sasanelli, Francesca, Khang Duy Ricky Le, Samuel Boon Ping Tay, Phong Tran, and Johan W. Verjans. "Applications of Natural Language Processing Tools in Orthopaedic Surgery: A Scoping Review." Applied Sciences 13, no. 20 (2023): 11586. http://dx.doi.org/10.3390/app132011586.
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The advent of many popular commercial forms of natural language processing tools has changed the way we can utilise digital technologies to tackle problems with big data. The objective of this review is to evaluate the current research and landscape of natural language processing tools and explore their potential use and impact in the field of orthopaedic surgery. In doing so, this review aims to answer the research question of how NLP tools can be utilised to streamline processes within orthopedic surgery. To do this, a scoping review was performed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and Arksey and O’Malley framework for scoping reviews, as well as a computer-assisted literature search on the Medline, Embase and Google Scholar databases. Papers that evaluated the use of natural language processing tools in the field of orthopaedic surgery were included. Our literature search identified 24 studies that were eligible for inclusion. Our scoping review captured articles that highlighted multiple uses of NLP tools in orthopaedics. In particular, one study reported on the use of NLP for intraoperative monitoring, six for detection of adverse events, five for establishing orthopaedic diagnoses, two for assessing the patient experience, two as an informative resource for patients, one for predicting readmission, one for triaging, five for auditing and one for billing and coding. All studies assessed these various uses of NLP through its tremendous computational ability in extracting structured and unstructured text from the medical record, including operative notes, pathology and imaging reports, and progress notes, for use in orthopaedic surgery. Our review demonstrates that natural language processing tools are becoming increasingly studied for use and integration within various processes of orthopaedic surgery. These AI tools offer tremendous promise in improving efficiency, auditing and streamlining tasks through their immense computational ability and versatility. Despite this, further research to optimise and adapt these tools within the clinical environment, as well as the development of evidence-based policies, guidelines and frameworks are required before their wider integration within orthopaedics can be considered.
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Borisova, Nadezhda, and Elena Karashtranova. "Converting Numeral Text in Bulgarian into Digit Number Using GATE." Mathematics and Informatics LXV, no. 3 (2022): 231–46. http://dx.doi.org/10.53656/math2022-3-2-con.
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The Internet serves billions of users providing a variety of information resources whereby a lot of the information is presented in natural human language and needs an efficient approach to be processed. Natural language processing (NLP) refers to the ability of computers to analyze and understand the structure of human language. By utilizing NLP this linguistic knowledge is transformed into algorithms for solving specific problems. GATE is widely used, open-source software infrastructure that provides a framework and components for solving NLP tasks. The available GATE tools can be adapted to other languages and text processing tasks. This article will present an approach for converting numeric data, written as words in Bulgarian, into digit numbers. For this case, a relevant configuration file for Bulgarian has been integrated into the general tool set in the open source software for natural language processing GATE. The aim of this survey is to determine the exact numeric value of Bulgarian text numeric data, which can be used as a starting point for producing more complex annotations, such as monetary measurement units, etc.