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Relevant bibliographies by topics / Natural language processing techniques

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Academic literature on the topic 'Natural language processing techniques'

Author: Grafiati

Published: 10 December 2022

Last updated: 31 July 2025

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Journal articles on the topic "Natural language processing techniques"

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Yilmaz, A. Egemen. "Natural Language Processing." International Journal of Systems and Service-Oriented Engineering 4, no. 1 (2014): 68–83. http://dx.doi.org/10.4018/ijssoe.2014010105.

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Requirement analysis is the very first and crucial step in the software development processes. On the other hand, as previously addressed by other researchers, it is the Achilles' heel of the whole process since the requirements lie on the problem space, whereas other software artifacts are on the solution space. Stating the requirements in a clear manner eases the following steps in the process as well as reducing the number of potential errors. In this paper, techniques for the improvement of the requirements expressed in the natural language are revisited. These techniques try to check the

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Researcher. "UNDERSTANDING NATURAL LANGUAGE PROCESSING (NLP) TECHNIQUES." International Journal of Computer Engineering and Technology (IJCET) 15, no. 4 (2024): 527–36. https://doi.org/10.5281/zenodo.13311223.

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Natural Language Processing (NLP) is a rapidly evolving field at the intersection of artificial intelligence, linguistics, and cognitive psychology. This article provides a comprehensive overview of NLP, exploring its core techniques, wide-ranging applications, and future directions. We delve into key NLP methods such as sentiment analysis, language generation, and named entity recognition, examining their underlying mechanisms and diverse applications. The impact of NLP across various sectors, including virtual assistants, translation services, healthcare, finance, and education, is thoroughl

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Researcher. "UNDERSTANDING NATURAL LANGUAGE PROCESSING (NLP) TECHNIQUES." International Journal of Research In Computer Applications and Information Technology (IJRCAIT) 15, no. 6 (2024): 1221–31. https://doi.org/10.5281/zenodo.14359554.

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Artificial intelligence, linguistics, and cognitive psychology all come together in Natural Language Processing (NLP), an area that is changing quickly. This article discusses NLP's main methods, practical uses, and possible future developments. Key NLP techniques like sentiment analysis, language generation, and named object recognition are studied in depth and have various uses. NLP's Significant effects are discussed in many areas, such as virtual helpers, translation services, healthcare, finance, and education. NLP has come quite a way, but it still has issues with unclear words, understa

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Madhukar Jukanti. "Understanding Natural Language Processing (NLP) Techniques." Journal of Computer Science and Technology Studies 7, no. 7 (2025): 1005–12. https://doi.org/10.32996/jcsts.2025.7.7.112.

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Natural Language Processing (NLP) is a transformative field that integrates computational intelligence with human language through sophisticated algorithms. This paper explores foundational mechanisms that enable machines to understand, translate, and infer human language across various applications. Key processing techniques include tokenization using compression-based subword segmentation and Named Entity Recognition (NER) employing BiLSTM-CNN architectures for high-accuracy entity tagging. Sentiment analysis utilizes convolutional neural networks and transformer-based encoders to extract nu

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Shamsitdinova, M. "Incorporating Natural Language Processing Technologies for Personalized Language Learning Experience." International Journal of Law And Criminology 5, no. 5 (2025): 79–80. https://doi.org/10.37547/ijlc/volume05issue05-11.

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This paper explores the application of Natural Language Processing (NLP) technologies in delivering personalized language learning experiences. As language learning evolves with digital transformation, NLP stands out as a powerful tool for customizing content, automating feedback, and enhancing learner engagement. The study discusses current NLP techniques, their integration into language education platforms, and the pedagogical benefits of adaptive learning systems. It concludes with challenges and future prospects of implementing NLP in educational settings.

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Shiva, Shrishti, Mohamed El-Dosuky, and Sherif Kamel. "Natural Language Processing and Natural Language Understanding Techniques for Intelligent Search." International Journal of Computer Applications 186, no. 11 (2024): 39–45. http://dx.doi.org/10.5120/ijca2024923468.

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Jayanth, Chikkarangaiah, Uday Adarsh, De Hilster David, Gangadhar Shobha, and Shetty Jyoti. "Enhancing the English natural language processing dictionary using natural language processing++." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 3 (2024): 3466–77. https://doi.org/10.11591/ijai.v13.i3.pp3466-3477.

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Every natural language-based project requires the use of an English dictionary. But the current English dictionaries are not updated as the English language is constantly evolving. The English dictionary used for natural language processing (NLP) projects needs to be enhanced by adding more words and phrases. This helps in improving the accuracy of NLP applications such as machine translation, performance of text analysis, recognition, and part of speech (POS) tagging. Several approaches are proposed in this direction, this paper develops and demonstrates enhancement of the English dictionary

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Valiyev, Giavid, Marcello Piraino, Arvid Kok, Michael Street, Ivana Ilic Mestric, and Retzius Birger. "Initial Exploitation of Natural Language Processing Techniques on NATO Strategy and Policies." Information & Security: An International Journal 47, no. 2 (2020): 187–202. http://dx.doi.org/10.11610/isij.4713.

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Chikkarangaiah, Jayanth, Adarsh Uday, David De Hilster, Shobha Gangadhar, and Jyoti Shetty. "Enhancing the English natural language processing dictionary using natural language processing++." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 3 (2024): 3466. http://dx.doi.org/10.11591/ijai.v13.i3.pp3466-3477.

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<p><span lang="EN-US">Every natural language-based project requires the use of an English dictionary. But the current English dictionaries are not updated as the English language is constantly evolving. The English dictionary used for natural language processing (NLP) projects needs to be enhanced by adding more words and phrases. This helps in improving the accuracy of NLP applications such as machine translation, performance of text analysis, recognition, and part of speech (POS) tagging. Several approaches are proposed in this direction, this paper develops and demonstrates enha

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H.Yousif, Jabar. "Natural Language Processing based Soft Computing Techniques." International Journal of Computer Applications 77, no. 8 (2013): 43–49. http://dx.doi.org/10.5120/13418-1089.

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Dissertations / Theses on the topic "Natural language processing techniques"

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Cosh, Kenneth John. "Supporting organisational semiotics with natural language processing techniques." Thesis, Lancaster University, 2003. http://eprints.lancs.ac.uk/12351/.

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Harmain, H. M. "Building object-oriented conceptual models using natural language processing techniques." Thesis, University of Sheffield, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.312740.

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Califf, Mary Elaine. "Relational learning techniques for natural language information extraction /." Digital version accessible at:, 1998. http://wwwlib.umi.com/cr/utexas/main.

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Eyecioglu, Ozmutlu Asli. "Paraphrase identification using knowledge-lean techniques." Thesis, University of Sussex, 2016. http://sro.sussex.ac.uk/id/eprint/65497/.

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This research addresses the problem of identification of sentential paraphrases; that is, the ability of an estimator to predict well whether two sentential text fragments are paraphrases. The paraphrase identification task has practical importance in the Natural Language Processing (NLP) community because of the need to deal with the pervasive problem of linguistic variation. Accurate methods for identifying paraphrases should help to improve the performance of NLP systems that require language understanding. This includes key applications such as machine translation, information retrieval an

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Chong, Man Yan Miranda. "A study on plagiarism detection and plagiarism direction identification using natural language processing techniques." Thesis, University of Wolverhampton, 2013. http://hdl.handle.net/2436/298219.

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Ever since we entered the digital communication era, the ease of information sharing through the internet has encouraged online literature searching. With this comes the potential risk of a rise in academic misconduct and intellectual property theft. As concerns over plagiarism grow, more attention has been directed towards automatic plagiarism detection. This is a computational approach which assists humans in judging whether pieces of texts are plagiarised. However, most existing plagiarism detection approaches are limited to super cial, brute-force stringmatching techniques. If the text has

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Al, Qady Mohammed Abdelrahman. "Concept relation extraction using natural language processing the CRISP technique /." [Ames, Iowa : Iowa State University], 2008.

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Björner, Amanda. "Natural Language Processing techniques for feedback on text improvement : A qualitative study on press releases." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-301303.

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Press releases play a key role in today’s news production by being public statements of newsworthy content that function as a pre-formulation of news. Press releases originate from a wide range of actors, and a common goal is for them to reach a high societal impact. This thesis examines how Natural Language Processing (NLP) techniques can be successful in giving feedback to press release authors that help enhance the content and quality of their texts. This could, in turn, contribute to increased impact. To examine this, the research question is divided into two parts. The first part examines

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Peri, Deepthi. "Applying Natural Language Processing and Deep Learning Techniques for Raga Recognition in Indian Classical Music." Thesis, Virginia Tech, 2020. http://hdl.handle.net/10919/99967.

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In Indian Classical Music (ICM), the Raga is a musical piece's melodic framework. It encompasses the characteristics of a scale, a mode, and a tune, with none of them fully describing it, rendering the Raga a unique concept in ICM. The Raga provides musicians with a melodic fabric, within which all compositions and improvisations must take place. Identifying and categorizing the Raga is challenging due to its dynamism and complex structure as well as the polyphonic nature of ICM. Hence, Raga recognition—identify the constituent Raga in an audio file—has become an important problem in music inf

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Imperatore, Gennaro. "Improving ease and speed of use of mobile augmentative and alternative communication systems through the use of natural language processing and natural language generation techniques." Thesis, University of Strathclyde, 2016. http://digitool.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=27381.

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Communication is recognised as a human right by the United Nations. Currently there are millions of people who for a variety of reasons cannot communicate comfortably. For example, in the UK alone there are 250,000 people who as a result of a stroke are now unable to communicate and are affected by a condition known as Aphasia. These people are said to have Complex Communication Needs. With the proliferation of smart devices like tablets and Smartphone, people with Complex Communication Needs are discovering the assistive potential of these devices to aid them either in the recuperation of the

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Antici, Francesco. "Advanced techniques for cross-language annotation projection in legal texts." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/23884/.

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Nowadays, the majority of the services we benefit from, are provided online and their use is regulated by the acceptance to the terms of service by the users. All our data are handled accordingly with the clauses of such document and all our behaviours must comply with it. Given so, it would be very useful to find automated techniques to ensure fairness of the document or inform the users about possible threats. The focus of this work, is to create resources aimed to the development of such tools in languages other than English, which may lack in linguistic resources and annotated corpus. T

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Books on the topic "Natural language processing techniques"

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Perez-Marin, Diana. Conversational agents and natural language interaction: Techniques and effective practices. Information Science Reference, 2011.

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American Association for Artificial Intelligence. and AAAI Workshop (1992 : Stanford, California), eds. Statistically-based natural language programming techniques: Papers from the 1992 AAAI Workshop, March 25-27, Stanford University. AAAI Press, 1992.

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Harrington, Jonathan. Techniques in speech acoustics. Kluwer Academic Publishers, 1999.

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C, Rothman Raymond. Programming in your words, expert or novice: With any database program, custom-make your programs, linguistic technique, filing and accounting system. Database Users & Information Managers International, 1988.

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Filgueiras, M., L. Damas, N. Moreira, and A. P. Tomás, eds. Natural Language Processing. Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/3-540-53678-7.

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Lee, Raymond S. T. Natural Language Processing. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-1999-4.

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Lee, Raymond. Natural Language Processing. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-3208-4.

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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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Kulkarni, Akshay, and Adarsha Shivananda. Natural Language Processing Recipes. Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-7351-7.

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Kulkarni, Akshay, Adarsha Shivananda, and Anoosh Kulkarni. Natural Language Processing Projects. Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-7386-9.

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Book chapters on the topic "Natural language processing techniques"

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Kaur, Ravleen, and M. P. S. Bhatia. "Managing Uncertainty in NLP: Advanced Techniques and Approaches." In Transformative Natural Language Processing. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-88988-2_5.

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Bethu, Srikanth, Suresh Mamidisetti, S. Bhargavi Latha, and B. Sankara Babu. "S2S Translator by Natural Language Processing." In Smart Computing Techniques and Applications. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0878-0_47.

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Fujita, Atsushi. "Natural language processing techniques for translation." In Metalanguages for Dissecting Translation Processes. Routledge, 2022. http://dx.doi.org/10.4324/9781003250852-19.

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Korkontzelos, Ioannis, Ioannis P. Klapaftis, and Suresh Manandhar. "Reviewing and Evaluating Automatic Term Recognition Techniques." In Advances in Natural Language Processing. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-85287-2_24.

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Bouamor, Houda, Aurélien Max, and Anne Vilnat. "Comparison of Paraphrase Acquisition Techniques on Sentential Paraphrases." In Advances in Natural Language Processing. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14770-8_9.

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Rodzin, Sergey, Victoria Bova, Yury Kravchenko, and Lada Rodzina. "Deep Learning Techniques for Natural Language Processing." In Artificial Intelligence Trends in Systems. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09076-9_11.

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Ghosh, Sujan, Sangeet Bose, and Anupam Mondal. "Paraphrase Detection Techniques in Natural Language Processing." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-3287-9_6.

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Chang, Yi, Hongbo Xu, and Shuo Bai. "A Re-examination of IR Techniques in QA System." In Natural Language Processing – IJCNLP 2004. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-540-30211-7_8.

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Díaz, Isabel, Lidia Moreno, Inmaculada Fuentes, and Oscar Pastor. "Integrating Natural Language Techniques in OO-Method." In Computational Linguistics and Intelligent Text Processing. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-540-30586-6_60.

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Wulff, Peter, Marcus Kubsch, and Christina Krist. "Natural Language Processing and Large Language Models." In Springer Texts in Education. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-74227-9_7.

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Abstract In this chapter we introduce the basics of natural language processing techniques that are important to systematically analyze language data. In particular, we will utilize simple large language models and showcase examples of how to apply them in science education research contexts. We will also point to recently advanced large language models that are capable of solving problems without further training, which opens up novel potentials (and challenges) for science education research.

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Conference papers on the topic "Natural language processing techniques"

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Salau, Ayodeji Olalekan, Esuku Dickson Emmanuel, Ahmed Alemran, Chandra Kumar Dixit, and Sepiribo Lucky Braide. "Exploring Large Language Models for Natural Language Processing." In 2024 Second International Conference Computational and Characterization Techniques in Engineering & Sciences (IC3TES). IEEE, 2024. https://doi.org/10.1109/ic3tes62412.2024.10877621.

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Shetty, Keerthi, Swati Tyagi, Anupama Jha, D. N. Murali Krishna Rao, J. R. Arunkumar, and Lakshmikanth R. "Natural Language Processing in Strategic Planning Analysis." In 2024 Second International Conference Computational and Characterization Techniques in Engineering & Sciences (IC3TES). IEEE, 2024. https://doi.org/10.1109/ic3tes62412.2024.10877514.

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Punyatoya, Swastisudha, Chittaranjan Pradhan, and Abhaya Kumar Sahoo. "Exploration of Text Classification Techniques in Natural Language Processing." In 2024 4th International Conference on Computer, Communication, Control & Information Technology (C3IT). IEEE, 2024. https://doi.org/10.1109/c3it60531.2024.10829488.

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Hemalatha, Raavi, Godi Amulya, and C. H. S. N. Sai Lalitha. "Natural Language Processing for Analyzing Social Media Trends." In 2024 Second International Conference Computational and Characterization Techniques in Engineering & Sciences (IC3TES). IEEE, 2024. https://doi.org/10.1109/ic3tes62412.2024.10877677.

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Murthy, Anantha, Pallavi Sagar Deshpande, Shweta Gakhreja, Yogita Satish Garwal, Neetu Jain, and Rajeswari Katare. "Natural Language Processing for Sentiment Analysis in Marketing." In 2024 Second International Conference Computational and Characterization Techniques in Engineering & Sciences (IC3TES). IEEE, 2024. https://doi.org/10.1109/ic3tes62412.2024.10877630.

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Jayakumar, V. Moses, R. Rajakumari, Purnachandra Rao Alapati, Santiago Otero-Potosi, D. Naga Malleswari, and M. Karthik. "Enhancing English Language Assessment in Educational Settings using Natural Language Processing Techniques." In 2025 3rd International Conference on Intelligent Systems, Advanced Computing and Communication (ISACC). IEEE, 2025. https://doi.org/10.1109/isacc65211.2025.10969428.

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Das, Satyesh, and Divyesh Das. "Natural Language Processing (NLP) Techniques: Usability in Human-Computer Interactions." In 2024 6th International Conference on Natural Language Processing (ICNLP). IEEE, 2024. http://dx.doi.org/10.1109/icnlp60986.2024.10692776.

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Kumar, M. Vinoth, Vichitra Sivaji, B. V. V. Padmavathi, N. Jothi, B. Muthulakshmi, and V. R. Yasu Bharathi. "Natural Language Processing Techniques for Enhancing Automated Essay Scoring Systems." In 2024 Second International Conference Computational and Characterization Techniques in Engineering & Sciences (IC3TES). IEEE, 2024. https://doi.org/10.1109/ic3tes62412.2024.10877454.

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Singh, Shubhanshu, Shraddha Panda, Aditya Pratap Singh, and A. Anilet Bala. "First-Aid Medykit Assistant Chatbot using Natural Language Processing Techniques." In 2024 International Conference on Communication, Computing and Energy Efficient Technologies (I3CEET). IEEE, 2024. https://doi.org/10.1109/i3ceet61722.2024.10993701.

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Mohamed, Nachaat. "A Comprehensive Review of Natural Language Processing Techniques for Malware Detection." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10724079.

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Reports on the topic "Natural language processing techniques"

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Furey, John, Austin Davis, and Jennifer Seiter-Moser. Natural language indexing for pedoinformatics. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/41960.

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The multiple schema for the classification of soils rely on differing criteria but the major soil science systems, including the United States Department of Agriculture (USDA) and the international harmonized World Reference Base for Soil Resources soil classification systems, are primarily based on inferred pedogenesis. Largely these classifications are compiled from individual observations of soil characteristics within soil profiles, and the vast majority of this pedologic information is contained in nonquantitative text descriptions. We present initial text mining analyses of parsed text i

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Li, Eliot, Charles Nicholas, Tim Oates, and Raman K. Mehra. Intelligent Record Linkage Techniques Based on Information Retrieval, Natural Language Processing, and Machine Learning. Defense Technical Information Center, 2002. http://dx.doi.org/10.21236/ada408937.

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Durairaj Rajeswari, Jyothi Prasanth. Leveraging Machine Learning and Natural Language Processing Techniques for Agriculture Experiment Station Project Classification. Iowa State University, 2024. https://doi.org/10.31274/cc-20250502-47.

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Zinilli, Antonio. Text Mining in Action: Tools and Techniques using Python. Instats Inc., 2024. http://dx.doi.org/10.61700/k4powzm518m5z1739.

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This seminar provides a comprehensive exploration of text mining techniques using Python, tailored for academic researchers seeking to analyze large textual datasets effectively. Participants will gain hands-on experience with Python libraries and methodologies for natural language processing, sentiment analysis, topic modeling, text classification, and more, enhancing their data analysis capabilities across various disciplines.

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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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Pasupuleti, Murali Krishna. AI-Driven Marketing Innovations: Personalization and Ethics in the Digital Era. National Education Services, 2025. https://doi.org/10.62311/nesx/rr625.

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Abstract: This article explores the transformative impact of artificial intelligence (AI) on digital marketing, focusing on strategies for delivering personalized content and ensuring ethical advertising. By leveraging AI, marketers can now analyze consumer behavior with precision, enabling targeted content, automated ad placement, and real-time adjustments that enhance user engagement and conversions. The Article examines foundational AI techniques, such as recommendation engines, predictive analytics, and natural language processing, which drive personalization at scale. Additionally, it add

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Li, Chao, Alexander Ryota Keeley, Shunsuke Mangi, and Satoru Yamadera. The Environmental, Social, and Governance Emphasis of Leading Companies in East Asia and Southeast Asia Unveiled by Deep Learning. Asian Development Bank, 2025. https://doi.org/10.22617/wps250262-2.

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This paper draws on an analysis of environmental, social, and governance topics in corporate reports in East Asia and Southeast Asia. It notes that economics and governance risk were the most frequently mentioned, but with significant variations across the region. The analysis looked at 480 reports published in 2023 by 293 companies. It included firms from the People’s Republic of China, Japan, the Republic of Korea, Indonesia, Malaysia, the Philippines, Singapore, and Thailand, focusing on those with the largest market capitalizations. It examined 13 environmental, social, and governance topi

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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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Steedman, Mark. Natural Language Processing. Defense Technical Information Center, 1994. http://dx.doi.org/10.21236/ada290396.

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Bergeaud, Antonin, Adam Jaffe, and Dimitris Papanikolaou. Natural Language Processing and Innovation Research. National Bureau of Economic Research, 2025. https://doi.org/10.3386/w33821.

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