Relevant bibliographies by topics / Natural Language Processes (NLP)

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Natural Language Processes (NLP)'

Author: Grafiati
Published: 2 June 2025
Last updated: 31 July 2025

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

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

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Natural Language Processing (NLP) has undergone a revolution with the emergence of large language models (LLMs) like GPT and BERT. Prompt engineering, the art of crafting effective inputs to guide LLMs, has become a cornerstone of modern NLP applications. This paper explores how prompt engineering is streamlining NLP processes, revolutionizing industries such as banking, and addressing the inherent risks associated with generative AI. Through practical examples and a discussion of potential pitfalls, we offer strategies for mitigating challenges and maximizing the benefits of prompt engineerin
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Dallo, Khan Ali Marwani. "Natural language processing for business analytics." Advances in Engineering Innovation 3, no. 1 (2023): 37–40. http://dx.doi.org/10.54254/2977-3903/3/2023038.

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Natural Language Processing (NLP), a branch of artificial intelligence, is gaining traction as a potent tool for business analytics. With the proliferation of unstructured textual data, businesses are actively seeking methodologies to distill valuable insights from vast textual repositories. The introduction of NLP in the realm of business analytics offers a transformative approach, automating traditional manual processes and fostering real-time, data-driven decision-making. From sentiment analysis to text summarization, NLP is facilitating businesses in deciphering consumer feedback, predicti
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Abjalova, Manzura. "Lemmatization and stemming processes in automatic morphological analysis of Uzbek mants." Uzbekistan: language and culture 3, no. 3 (2024): 6–21. https://doi.org/10.52773/tsuull.uzlc.2024.3/hvmn8509.

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In the field of natural language processing, the stages of graphematic analysis (tokenization), morphological analysis (lemmatization and stemming), syntactic analysis (parsing), and semantic analysis are important for almost all areas of NLP. It is possible to create many software applications from processed natural language for digital technology. In NLP, the lemmatization and stemming technologies of morphological analysis are common to all languages, and they determine the normal form of word forms in the dictionary. Although the task of lemmatization and stemming is the same, they differ
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Simanullang, Gerald Shan Benediktus, and Jin Ai The. "Roles of Natural Language Processing in New Product Development Process: Literature Review." Jurnal Rekayasa Sistem Industri 13, no. 1 (2024): 117–30. http://dx.doi.org/10.26593/jrsi.v13i1.6790.117-130.

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Customer satisfaction is a key success factor for a business. To provide products that meet customer satisfaction, companies must be able to understand the customers’ needs and desires. Technological developments nowadays have helped companies to understand customer desires more easily so that companies can provide products that satisfy their customer. Natural Language Processing (NLP) is a technology that allows computers to process human language. NLP is also commonly referred as text-mining. NLP has been utilized in the New Product Development (NPD) process. We compiled studies related to N
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Khan, Noel, David Elizondo, Lipika Deka, and Miguel A. Molina-Cabello. "Natural Language Processing Tools and Workflows for Improving Research Processes." Applied Sciences 14, no. 24 (2024): 11731. https://doi.org/10.3390/app142411731.

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The modern research process involves refining a set of keywords until sufficiently pertinent results are obtained from acceptable sources. References and citations from the most relevant results can then be traced to related works. This process iteratively develops a set of keywords to find the most relevant literature. However, because a keyword-based search essentially samples a corpus, it may be inadequate for capturing a broad or exhaustive understanding of a topic. Further, a keyword-based search is dependent upon the underlying storage and retrieval technology and is essentially a syntac
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Hasan, Zohaib, Zeba Vishwakarma, and Nidhi Pateriya. "NLP and its Components: A Detailed Discussion." International Journal of Innovative Research in Computer and Communication Engineering 11, no. 09 (2023): 10798–803. http://dx.doi.org/10.15680/ijircce.2023.1109034.

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Natural Language Processing (NLP) encompasses computational techniques for processing and analyzing human language, primarily through Natural Language Understanding (NLU) and Natural Language Generation (NLG). NLU focuses on interpreting language by analyzing phonology (sounds), morphology (word structures), syntax (sentence structures), semantics (meaning), and pragmatics (context). These processes enable machines to comprehend the nuances of human language for accurate interpretation and response generation. NLG, in contrast, involves producing human-like text from structured data. It includ
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S.Girirajan. "Code Generation Empowered by Natural Language Processing and Machine Learning Algorithms." Advances in Nonlinear Variational Inequalities 28, no. 1s (2024): 44–56. http://dx.doi.org/10.52783/anvi.v28.2186.

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The goal of this study is to revolutionize code creation processes by investigating the synergistic union of machine learning (ML) and natural language processing (NLP). Enterprising non-programmers with entrance barriers, traditional approaches to code generation frequently demand expert-level programming expertise. Development teams can communicate coding tasks in natural language by utilizing NLP techniques like language modeling and semantic parsing. This helps to close the gap between human intent and instructions that can be executed by a computer. By incorporating ML techniques, the sys
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Chung, Joohyun, Sangmin Song, and Heesook Son. "Exploring Natural Language Processing through an Exemplar Using YouTube." International Journal of Environmental Research and Public Health 21, no. 10 (2024): 1357. http://dx.doi.org/10.3390/ijerph21101357.

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There has been a growing emphasis on data across various health-related fields, not just in nursing research, due to the increasing volume of unstructured data in electronic health records (EHRs). Natural Language Processing (NLP) provides a solution by transforming this unstructured data into structured formats, thereby facilitating valuable insights. This methodology paper explores the application of NLP in nursing, using an exemplar case study that analyzes YouTube data to investigate social phenomena among adults living alone. The methodology involves five steps: accessing data through You
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Erik, Cambria. "A Review of Natural Language Processing Research." Engineering Computations 2017, no. 10 (2017): 10. https://doi.org/10.5281/zenodo.1000805.

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

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Oliveira, Mirna Fernanda de. "Enfrentamento do problema das divergências de tradução por um sistema de tradução automática : um exercício exploratório /." Araraquara : [s.n.], 2006. http://hdl.handle.net/11449/103605.

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Orientador: Bento Carlos Dias da Silva<br>Banca: Beatriz Nunes de Oliveira Longo<br>Banca: Dirce Charara Monteiro<br>Banca: Gladis Maria de Barcellos Almeida<br>Banca: Heronides Maurílio de Melo Moura<br>Resumo: O objetivo desta tese é desenvolver um estudo lingüístico-computacional exploratório de um problema específico que deve ser enfrentado por sistemas de tradução automática: o problema da divergências de tradução quer de natureza sintática quer de natureza léxico-semântica que se verificam entre pares de sentenças de línguas naturais diferentes. Para isso, fundamenta-se na metodologia de
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Oliveira, Mirna Fernanda de [UNESP]. "Enfrentamento do problema das divergências de tradução por um sistema de tradução automática: um exercício exploratório." Universidade Estadual Paulista (UNESP), 2006. http://hdl.handle.net/11449/103605.

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Made available in DSpace on 2014-06-11T19:32:47Z (GMT). No. of bitstreams: 0 Previous issue date: 2006-04-25Bitstream added on 2014-06-13T20:43:58Z : No. of bitstreams: 1 oliveira_mf_dr_ararafcl.pdf: 631650 bytes, checksum: fa4233637c661c5e993adcc08801d158 (MD5)<br>Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)<br>O objetivo desta tese é desenvolver um estudo lingüístico-computacional exploratório de um problema específico que deve ser enfrentado por sistemas de tradução automática: o problema da divergências de tradução quer de natureza sintática quer de natureza léxico
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Hellmann, Sebastian. "Integrating Natural Language Processing (NLP) and Language Resources Using Linked Data." Doctoral thesis, Universitätsbibliothek Leipzig, 2015. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-157932.

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This thesis is a compendium of scientific works and engineering specifications that have been contributed to a large community of stakeholders to be copied, adapted, mixed, built upon and exploited in any way possible to achieve a common goal: Integrating Natural Language Processing (NLP) and Language Resources Using Linked Data The explosion of information technology in the last two decades has led to a substantial growth in quantity, diversity and complexity of web-accessible linguistic data. These resources become even more useful when linked with each other and the last few years have see
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NOZZA, DEBORA. "Deep Learning for Feature Representation in Natural Language Processing." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2018. http://hdl.handle.net/10281/241185.

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La mole di dati generata dagli utenti sul Web è esponenzialmente cresciuta negli ultimi dieci anni, creando nuove e rilevanti opportunità per ogni tipo di dominio applicativo. Per risolvere i problemi derivanti dall’eccessiva quantità di dati, la ricerca nell’ambito dell’elaborazione del linguaggio naturale si è mossa verso lo sviluppo di modelli computazionali capaci di capirlo ed interpretarlo senza (o quasi) alcun intervento umano. Recentemente, questo campo di studi è stato testimone di un incremento sia in termini di efficienza computazionale che di risultati, per merito dell’avvento di
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Shao, Han. "Pretraining Deep Learning Models for Natural Language Understanding." Oberlin College Honors Theses / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=oberlin158955297757398.

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Sætre, Rune. "GeneTUC: Natural Language Understanding in Medical Text." Doctoral thesis, Norwegian University of Science and Technology, Department of Computer and Information Science, 2006. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-545.

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<p>Natural Language Understanding (NLU) is a 50 years old research field, but its application to molecular biology literature (BioNLU) is a less than 10 years old field. After the complete human genome sequence was published by Human Genome Project and Celera in 2001, there has been an explosion of research, shifting the NLU focus from domains like news articles to the domain of molecular biology and medical literature. BioNLU is needed, since there are almost 2000 new articles published and indexed every day, and the biologists need to know about existing knowledge regarding their own researc
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Panesar, Kulvinder. "Natural language processing (NLP) in Artificial Intelligence (AI): a functional linguistic perspective." Vernon Press, 2020. http://hdl.handle.net/10454/18140.

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Yes<br>This chapter encapsulates the multi-disciplinary nature that facilitates NLP in AI and reports on a linguistically orientated conversational software agent (CSA) (Panesar 2017) framework sensitive to natural language processing (NLP), language in the agent environment. We present a novel computational approach of using the functional linguistic theory of Role and Reference Grammar (RRG) as the linguistic engine. Viewing language as action, utterances change the state of the world, and hence speakers and hearer’s mental state change as a result of these utterances. The plan-based me
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Panesar, Kulvinder. "Conversational artificial intelligence - demystifying statistical vs linguistic NLP solutions." Universitat Politécnica de Valéncia, 2020. http://hdl.handle.net/10454/18121.

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yes<br>This paper aims to demystify the hype and attention on chatbots and its association with conversational artificial intelligence. Both are slowly emerging as a real presence in our lives from the impressive technological developments in machine learning, deep learning and natural language understanding solutions. However, what is under the hood, and how far and to what extent can chatbots/conversational artificial intelligence solutions work – is our question. Natural language is the most easily understood knowledge representation for people, but certainly not the best for computers beca
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Välme, Emma, and Lea Renmarker. "Accelerating Sustainability Report Assessment with Natural Language Processing." Thesis, Uppsala universitet, Avdelningen för visuell information och interaktion, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-445912.

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Corporations are expected to be transparent on their sustainability impact and keep their stakeholders informed about how large the impact on the environment is, as well as their work on reducing the impact in question. The transparency is accounted for in a, usually voluntary, sustainability report additional to the already required financial report. With new regulations for mandatory sustainability reporting in Sweden, comprehensive and complete guidelines for corporations to follow are insufficient and the reports tend to be extensive. The reports are therefore hard to assess in terms of ho
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Djoweini, Camran, and Henrietta Hellberg. "Approaches to natural language processing in app development." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-230167.

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Natural language processing is an on-going field that is not yet fully established. A high demand for natural language processing in applications creates a need for good development-tools and different implementation approaches developed to suit the engineers behind the applications. This project approaches the field from an engineering point of view to research approaches, tools, and techniques that are readily available today for development of natural language processing support. The sub-area of information retrieval of natural language processing was examined through a case study, where pr
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Books on the topic "Natural Language Processes (NLP)"

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Christodoulakis, Dimitris N., ed. Natural Language Processing — NLP 2000. Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-45154-4.

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Herbert, Robert K. Language universals, markedness theory, and natural phonetic processes. Mouton de Gruyter, 1986.

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International Conference on Natural Language Processing (2nd 2000 Patrai, Greece). Natural language processing - NLP 2000: Second International Conference, Patras, Greece, June 2-4, 2000 : proceedings. Springer, 2000.

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Oppentocht, Anna Linnea. Lexical semantic classification of Dutch verbs: Towards constructing NLP and human-friendly definitions. LEd, 1999.

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Gurevych, Iryna. The People’s Web Meets NLP: Collaboratively Constructed Language Resources. Springer Berlin Heidelberg, 2013.

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Chengqing, Zong, Chinese Association for Artificial Intelligence., IEEE Signal Processing Society, IEEE Systems, Man, and Cybernetics Society, and Institute of Electrical and Electronics Engineers. Beijing Section., eds. 2003 International conference on natural language processing and knowledge engineering: Proceedings : NLP-KE 2003 : Beijing, China. IEEE, 2003.

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International Conference on Natural Language Processing and Knowledge Engineering (2007 Beijing, China). Proceedings of International Conference on Natural Language Processing and Knowledge Engineering (NLP-KE'07) : Aug. 30-Sep. 1, Beijing China. IEEE, 2007.

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Loftsson, Hrafn, Eiríkur Rögnvaldsson, and Sigrún Helgadóttir, eds. Advances in natural language processing: 7th International Conference on NLP, IceTAL 2010, Reykjavik, Iceland, August 16-18, 2010 : proceedings. Springer, 2010.

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Kyoko, Kanzaki, and SpringerLink (Online service), eds. Advances in Natural Language Processing: 8th International Conference on NLP, JapTAL 2012, Kanazawa, Japan, October 22-24, 2012. Proceedings. Springer Berlin Heidelberg, 2012.

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Boschetti Adamczyk, Willian, Verónica Escudero, Hannah Liepmann, Simon Boehmer, and Isaure Delaporte. Developing a new method to uncover skills trends in emerging economies using online data and NLP techniques. ILO, 2025. https://doi.org/10.54394/hqqx3200.

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This research brief outlines a novel methodology using online big data and natural language processing (NLP) to analyze transferable skills trends across diverse sectors and countries, offering insights into skills demand, supply, mismatches, and their relationship to job quality and transitions.
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Book chapters on the topic "Natural Language Processes (NLP)"

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Kubsch, Marcus, Daniela Caballero, and Pablo Uribe. "Once More with Feeling: Emotions in Multimodal Learning Analytics." In The Multimodal Learning Analytics Handbook. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08076-0_11.

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AbstractThe emotions that students experience when engaging in tasks critically influence their performance and many models of learning and competence include assumptions about affective variables and respective emotions. However, while researchers agree about the importance of emotions for learning, it remains challenging to connect momentary affect, i.e., emotions, to learning processes. Advances in automated speech recognition and natural language processing (NLP) allow real time detection of emotions in recorded language. We use NLP and machine learning techniques to automatically extract
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Roy, Piyal, and Rajat Pandit. "Natural Language Processing Applications." In Advances in Computational Intelligence and Robotics. IGI Global, 2025. https://doi.org/10.4018/979-8-3693-6255-6.ch007.

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Natural language processing (NLP) stands at the forefront of innovation, revolutionizing communication between humans and machines. The chapter discusses practical applications across diverse domains such as healthcare, finance, customer service, social media analysis, e-commerce, legal, education, and journalism, emphasizing NLP's pivotal role in enhancing efficiency and decision-making processes. However, challenges like bias, data quality, and ethical concerns necessitate interdisciplinary collaboration for mitigation. Recent advances in deep learning, pre-trained language models, transfer
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Ghosh, Subhajit. "Natural Language Processing: Basics, Challenges, and Clustering Applications." In A Handbook of Computational Linguistics: Artificial Intelligence in Natural Language Processing. BENTHAM SCIENCE PUBLISHERS, 2024. http://dx.doi.org/10.2174/9789815238488124020006.

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Natural Language Processing (NLP) involves the use of algorithms and models and various computational techniques to analyze, process, and generate natural language data, including speech and text. NLP helps computers interact with humans in a more natural way, which has become increasingly important as more humancomputer interactions take place. NLP allows machines to process and analyze voluminous unstructured data, including social media posts, newspaper articles, reviews from customers, emails, and others. It helps organizations extract insights, automate tasks, and improve decision-making
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Liu, MQ. "Applying Natural Language Processing to Assessment." In Talent Assessment. Oxford University PressNew York, 2023. http://dx.doi.org/10.1093/oso/9780197611050.003.0005.

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Abstract Natural language processing (NLP), the enablement of machines to automatically process and learn human natural language data, is at the core of many real-world applications. Recent years have seen initial attempts to apply NLP in assessment. The goal of this chapter is to discuss how NLP can be applied to improve assessment in the selection context. The chapter opens with an introduction of NLP—what it is and how NLP techniques have evolved over time. It then dives into how various NLP methods can be applied in the assessment cycle, from assessment construction to analyzing assessment
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Kukana, Poonam, Sukhjinder Kaur, Ms Ashima, Chiman Saini, and Bhupinder Kaur. "NATURAL LANGUAGE PROCESSING IN CYBERSECURITY." In Artificial Intelligence and the Cybersecurity Revolution: Innovations and Implications. Iterative International Publishers (IIP), Selfypage Developers Pvt Ltd., 2025. https://doi.org/10.58532/nbennuraicr4.

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In this chapter, we will discuss the basics of Natural Language Processing (NLP) and its significance in Cyber security domain. It also discusses the application of Natural Language Processing (NLP) methods in cyber security, focusing on its increasing importance in detecting, preventing, and responding to cyber attacks. NLP approaches like text preprocessing; named entity recognition, sentiment analysis, and machine translation are utilized to process enormous amounts of unstructured text data from emails, logs, and forums. Major applications are phishing detection, threat intelligence, socia
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Jackson, G. Tanner, and Danielle S. McNamara. "Applying NLP Metrics to Students’ Self-Explanations." In Applied Natural Language Processing. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-60960-741-8.ch015.

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Intelligent Tutoring Systems (ITSs) are becoming an increasingly common method for students to engage with and learn course material. ITSs are designed to provide students with one-on-one learning that is tailored to their own pace and needs. These systems can adapt to each users’ individual knowledge and ability level to provide the most pedagogically effective learning environment. Tutoring systems have been designed that cover a variety of topics, including both well-defined and ill-defined domains. ITSs have seen great success within well-defined domains, where the topic itself provides on
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Rajendran, Rajesh Kanna, Mohana Priya T., and Karthick Chitrarasu. "Natural Language Processing (NLP) in Chatbot Design." In Design and Development of Emerging Chatbot Technology. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-1830-0.ch006.

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The creation and development of chatbots, which are the prevalent manifestations of artificial intelligence (AI) and machine learning (ML) technologies in today's digital world, are built on Natural Language Processing (NLP), which serves as a cornerstone in the process. This chapter investigates the significant part that natural language processing (NLP) plays in determining the development and effectiveness of chatbots, beginning with their beginnings as personal virtual assistants and continuing through their seamless incorporation into messaging platforms and smart home gadgets. The study
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Singh, Archana, Girish Lakhera, Megha Ojha, Amar Kumar Mishra, and Sanjay Kaushik. "Natural Language Processing for HR Chatbots and Virtual Assistants." In Advances in Logistics, Operations, and Management Science. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-3318-1.ch020.

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The purpose of this study is to explore fundamental NLP concepts, key techniques, and application across various HR processes. It addresses the challenges and limitations faced by NLP-driven HR chatbots and virtual assistants while presenting real-world success stories from leading organizations. The data for the present study was collected through secondary data formation from different articles and research papers available. To develop a theoretical framework, the literature on NLP, HR technology, and EHRM operations will be reviewed. This chapter underscores the significant role of NLP in t
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Dowell, Nia, and Vitomir Kovanović. "Modeling Educational Discourse with Natural Language Processing." In The Handbook of Learning Analytics, 2nd ed. SOLAR, 2022. http://dx.doi.org/10.18608/hla22.011.

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The broadening adoption of technology enhanced learning environments has substantially altered the manner in which educational communication takes place, with most people engaging in some form of online asynchronous or synchronous conversation every day. The language and discourse artifacts emerging from these technological environments is a rich source of information into learning processes and outcomes. This chapter describes the current landscape of natural language processing (NLP) tools and approaches available to researchers and practitioners to computationally discern patterns in large
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Singh, Arun Kumar, Ashish Tripathi, Sandeep Saxena, Pushpa Choudhary, Mahesh Kumar Singh, and Arjun Singh. "Impact of Deep Learning on Natural Language Processing." In Artificial Intelligence, Machine Learning and User Interface Design. BENTHAM SCIENCE PUBLISHERS, 2024. http://dx.doi.org/10.2174/9789815179606124010005.

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In the era of digitalization, electronic gadgets such as Google Translate, Siri, and Alexa have at least one characteristic: They are all the products of natural language processing (NLP). “Natural Language” refers to a human language used for daily communication, such as English, Hindi, Bengali, etc. Natural languages, as opposed to artificial languages such as computer languages and mathematical nomenclature, have evolved as they have been transmitted from generation to generation and are challenging to explain with clear limits in the first instance. In natural language processing, artifici
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Conference papers on the topic "Natural Language Processes (NLP)"

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Strelet, Eugeniu, Ivan Castillo, You Peng, et al. "Exploring Industrial Text Data for Monitoring Chemical Manufacturing Processes." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.101096.

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To address the limitations of traditional sensing instrumentation in industrial processes, this work explores the use of industrial text data. Given that current instrumentation often fails to capture the full scope of process-related information, text data resulting from operation of industrial settings (for example: maintenance, inspection and incident reports) can provide valuable insights. This study focuses on accessing the effectiveness of natural language processing (NLP) techniques in retrieving critical information from industrial text data. To achieve this, the classification of Proc
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Pabalkar, Sarika, Prashant Patel, Ramila Choudhary, Vanshika Panoch, Sahil Yadav, and Harsh Ghogale. "Resume Analyzer Using Natural Language Processing (NLP)." In 2024 International Conference on Intelligent Systems and Advanced Applications (ICISAA). IEEE, 2024. https://doi.org/10.1109/icisaa62385.2024.10828940.

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Otte, Lennart B., Christer Hogstrand, Adil Mardinoglu, and Miao Guo. "Multi-Omics biological embeddings for ML-models." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.136974.

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Machine learning algorithms have led to the development of numerous vector embeddings for biological entities such as metabolites, proteins, genes, and enzymes. However, these embeddings often lack contextual information due to their specialized focus on individual omics. Disease progression and biosynthesis pathways are increasingly understood through complex, multi-layered networks that integrate diverse omics data and intricate signaling and reaction sequences. Capturing these relationships in a meaningful way requires embeddings that account for both functional and multi-modal dependencies
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Braun, Daniel. "Teaching Natural Language Processing in Law School." In Proceedings of the Sixth Workshop on Teaching NLP. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.teachingnlp-1.13.

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McCrae, John. "Co-Creational Teaching of Natural Language Processing." In Proceedings of the Sixth Workshop on Teaching NLP. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.teachingnlp-1.5.

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Singh, Dipali. "Legal documents Text Analysis using Natural Language Processing (NLP)." In 2024 2nd International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS). IEEE, 2024. https://doi.org/10.1109/icssas64001.2024.10760929.

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Parde, Natalie. "Example-Driven Course Slides on Natural Language Processing Concepts." In Proceedings of the Sixth Workshop on Teaching NLP. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.teachingnlp-1.2.

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Krause, Lea, and Piek T. J. M. Vossen. "The Gricean Maxims in NLP - A Survey." In Proceedings of the 17th International Natural Language Generation Conference. Association for Computational Linguistics, 2024. https://doi.org/10.18653/v1/2024.inlg-main.39.

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Sun, Jinghua, Samuel Edwards, Nic Connelly, Andrew Bridge, and Lei Zhang. "Airborne Software Development Processes Certification Review Strategy based on RTCA/DO-178C." In 10th International Conference on Natural Language Processing (NLP 2021). Academy and Industry Research Collaboration Center (AIRCC), 2021. http://dx.doi.org/10.5121/csit.2021.112321.

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functionality for aircraft. Highly complex software, however, cannot be exhaustively tested and only assured through a structured, process, activity, and objective-based approach. This paper studied the development processes and objectives applicable to different software levels based on RTCA/DO-178C, and identified 82 technical focus points based on each airborne software development sub-process, then created a Process Technology Coverage matrix to demonstrate the technical focuses of each process. This paper proposes an objective-oriented top-down and bottom-up sampling strategy for the four
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Sintoris, Konstantinos, and Kostas Vergidis. "Extracting Business Process Models Using Natural Language Processing (NLP) Techniques." In 2017 IEEE 19th Conference on Business Informatics (CBI). IEEE, 2017. http://dx.doi.org/10.1109/cbi.2017.41.

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Reports on the topic "Natural Language Processes (NLP)"

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Xu, Chao, Walter Forkel, Stefan Borgwardt, Franz Baader, and Beihai Zhou. Automatic Translation of Clinical Trial Eligibility Criteria into Formal Queries. Technische Universität Dresden, 2019. http://dx.doi.org/10.25368/2023.224.

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Selecting patients for clinical trials is very labor-intensive. Our goal is to develop an automated system that can support doctors in this task. This paper describes a major step towards such a system: the automatic translation of clinical trial eligibility criteria from natural language into formal, logic-based queries. First, we develop a semantic annotation process that can capture many types of clinical trial criteria. Then, we map the annotated criteria to the formal query language. We have built a prototype system based on state-of-the-art NLP tools such as Word2Vec, Stanford NLP tools,
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Liang, Xiao. Analyzing the Amazon Shopping Experience: A Sentiment Analysis Based on Natural Language Processing (NLP) and Model Comparison. Iowa State University, 2024. http://dx.doi.org/10.31274/cc-20240624-215.

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Alonso-Robisco, Andres, and Jose Manuel Carbo. Analysis of CBDC Narrative OF Central Banks using Large Language Models. Banco de España, 2023. http://dx.doi.org/10.53479/33412.

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Central banks are increasingly using verbal communication for policymaking, focusing not only on traditional monetary policy, but also on a broad set of topics. One such topic is central bank digital currency (CBDC), which is attracting attention from the international community. The complex nature of this project means that it must be carefully designed to avoid unintended consequences, such as financial instability. We propose the use of different Natural Language Processing (NLP) techniques to better understand central banks’ stance towards CBDC, analyzing a set of central bank discourses f
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Alonso-Robisco, Andrés, Andrés Alonso-Robisco, José Manuel Carbó, et al. Empowering financial supervision: a SupTech experiment using machine learning in an early warning system. Banco de España, 2025. https://doi.org/10.53479/39320.

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New technologies have made available a vast amount of new data in the form of text, recording an exponentially increasing share of human and corporate behavior. For financial supervisors, the information encoded in text is a valuable complement to the more traditional balance sheet data typically used to track the soundness of financial institutions. In this study, we exploit several natural language processing (NLP) techniques as well as network analysis to detect anomalies in the Spanish corporate system, identifying both idiosyncratic and systemic risks. We use sentiment analysis at the cor
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Burstein, Jill, Geoffrey LaFlair, Antony Kunnan, and Alina von Davier. A Theoretical Assessment Ecosystem for a Digital-First Assessment - The Duolingo English Test. Duolingo, 2022. http://dx.doi.org/10.46999/kiqf4328.

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The Duolingo English Test is a groundbreaking, digital­first, computer­adaptive measure of English language proficiency for communication and use in English­medium settings. The test measures four key English language proficiency constructs: Speaking, Writing, Reading, and Listening (SWRL), and is aligned with the Common European Framework of Reference for Languages (CEFR) proficiency levels and descriptors. As a digital­first assessment, the test uses “human­in­the­loop AI” from end to end for test security, automated item generation, and scoring of test­taker responses. This paper presents a
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Avellán, Leopoldo, and Steve Brito. Crossroads in a Fog: Navigating Latin America's Development Challenges with Text Analytics. Inter-American Development Bank, 2023. http://dx.doi.org/10.18235/0005489.

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Latin America and the Caribbean are facing challenging times due to a combination of worsening development gaps and limited fiscal space to address them. Furthermore, the region is contending with an unfavorable external environment. Issues such as rising poverty, climate change, inadequate infrastructure, and low-quality education and health services, among others, require immediate attention. Deciding how to prioritize efforts to address these development gaps is challenging due to their complexity and urgency, and setting priorities becomes even more difficult when resources are limited. Th
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Pasupuleti, Murali Krishna. Decentralized Creativity: AI-Infused Blockchain for Secure and Transparent Digital Innovation. National Education Services, 2025. https://doi.org/10.62311/nesx/rrvi125.

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Abstract The convergence of artificial intelligence (AI) and blockchain technology is transforming the creative economy by enabling secure, transparent, and decentralized innovation in digital content creation, intellectual property management, and monetization. Traditional creative industries are often constrained by centralized platforms, opaque copyright enforcement, and unfair revenue distribution, which limit the autonomy and financial benefits of creators. By leveraging blockchain’s immutable ledger, smart contracts, and non-fungible tokens (NFTs), digital assets can be authenticated, to
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JOSI, Editor. How GPT Enhances Soft Systems Methodologies in Industrial Engineering. Industrial Engineering Department, Faculty of Engineering, Universitas Andalas, 2024. https://doi.org/10.25077/11122024-2.

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Industrial systems are rarely straightforward. They often involve complex, ill-structured problems influenced by human factors, organizational culture, and decision-making dynamics. These types of challenges, often addressed through soft systems methodologies, require innovative approaches to bridge gaps between technology, people, and processes. Generative Pre-trained Transformers (GPT), with their advanced natural language understanding capabilities, have emerged as a promising tool for optimizing soft systems in industrial engineering. In this post, we explore how GPT can support soft syste
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Lindo-Ocampo, Gloria Inés, and Hilda Clarena Buitrago-García. English for Business Course. Thematic Unit: Business Events. Ediciones Universidad Cooperativa de Colombia, 2022. http://dx.doi.org/10.16925/gcnc.24.

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This didactic unit is aimed at the fifth semester students of the Business Administration, Marketing and International Business program, who have already completed the four basic levels of the Open Lingua program. This proposal seeks to develop skills and competencies that allow them to perform in different fields related to private, public and solidarity economy companies, and in various mediation and negotiation processes at national and international levels. The instructional design of this unit contains real-life situations, focused on the world of business, that allow students to interact
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Or, Etti, David Galbraith, and Anne Fennell. Exploring mechanisms involved in grape bud dormancy: Large-scale analysis of expression reprogramming following controlled dormancy induction and dormancy release. United States Department of Agriculture, 2002. http://dx.doi.org/10.32747/2002.7587232.bard.

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The timing of dormancy induction and release is very important to the economic production of table grape. Advances in manipulation of dormancy induction and dormancy release are dependent on the establishment of a comprehensive understanding of biological mechanisms involved in bud dormancy. To gain insight into these mechanisms we initiated the research that had two main objectives: A. Analyzing the expression profiles of large subsets of genes, following controlled dormancy induction and dormancy release, and assessing the role of known metabolic pathways, known regulatory genes and novel se
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