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Németh, Renáta, and Júlia Koltai. "Natural language processing." Intersections 9, no. 1 (April 26, 2023): 5–22. http://dx.doi.org/10.17356/ieejsp.v9i1.871.
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Natural language processing (NLP) methods are designed to automatically process and analyze large amounts of textual data. The integration of this new-generation toolbox into sociology faces many challenges. NLP was institutionalized outside of sociology, while the expertise of sociology has been based on its own methods of research. Another challenge is epistemological: it is related to the validity of digital data and the different viewpoints associated with predictive and causal approaches. In our paper, we discuss the challenges and opportunities of the use of NLP in sociology, offer some potential solutions to the concerns and provide meaningful and diverse examples of its sociological application, most of which are related to research on Eastern European societies. The focus will be on the use of NLP in quantitative text analysis. Solutions are provided concerning how sociological knowledge can be incorporated into the new methods and how the new analytical tools can be evaluated against the principles of traditional quantitative methodology.
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Ali, Miss Aliya Anam Shoukat. "AI-Natural Language Processing (NLP)." International Journal for Research in Applied Science and Engineering Technology 9, no. VIII (August 10, 2021): 135–40. http://dx.doi.org/10.22214/ijraset.2021.37293.
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Natural Language Processing (NLP) could be a branch of Artificial Intelligence (AI) that allows machines to know the human language. Its goal is to form systems that can make sense of text and automatically perform tasks like translation, spell check, or topic classification. Natural language processing (NLP) has recently gained much attention for representing and analysing human language computationally. It's spread its applications in various fields like computational linguistics, email spam detection, information extraction, summarization, medical, and question answering etc. The goal of the Natural Language Processing is to style and build software system which will analyze, understand, and generate languages that humans use naturally, so as that you just could also be ready to address your computer as if you were addressing another person. Because it’s one amongst the oldest area of research in machine learning it’s employed in major fields like artificial intelligence speech recognition and text processing. Natural language processing has brought major breakthrough within the sector of COMPUTATION AND AI.
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Rohit Kumar Yadav, Aanchal Madaan, and Janu. "Comprehensive analysis of natural language processing." Global Journal of Engineering and Technology Advances 19, no. 1 (April 30, 2024): 083–90. http://dx.doi.org/10.30574/gjeta.2024.19.1.0058.
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Natural Language Processing (NLP) is a fascinating field of study that teaches computers to understand and use human language. This means that computers can read, write, and even translate text just like humans. NLP has many practical uses, such as categorizing text, identifying the tone of language, recognizing names in text, translating languages, and answering questions. NLP has come a long way since it was first developed. In the past, it relied on strict rules to understand language, but now it uses advanced techniques like machine learning and deep learning to understand text. However, there are still some challenges in NLP, such as understanding the meaning of words in context and considering cultural differences. Despite these challenges, NLP is being used in many different areas, from healthcare and finance to education and customer service. NLP is transforming the way humans interact with computers and is making it easier to extract important information from large amounts of text.
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Sadiku, Matthew N. O., Yu Zhou, and Sarhan M. Musa. "NATURAL LANGUAGE PROCESSING IN HEALTHCARE." International Journal of Advanced Research in Computer Science and Software Engineering 8, no. 5 (June 2, 2018): 39. http://dx.doi.org/10.23956/ijarcsse.v8i5.626.
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Natural language processing (NLP) refers to the process of using of computer algorithms to identify key elements in everyday language and extract meaning from unstructured spoken or written communication. Healthcare is the biggest user of the NLP tools. It is expected that NLP tools should be able to bridge the gap between the mountain of data generated daily and the limited cognitive capacity of the human mind. This paper provides a brief introduction on the use of NLP in healthcare.
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Alharbi, Mohammad, Matthew Roach, Tom Cheesman, and Robert S. Laramee. "VNLP: Visible natural language processing." Information Visualization 20, no. 4 (August 13, 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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Al-Khalifa, Hend S., Taif AlOmar, and Ghala AlOlyyan. "Natural Language Processing Patents Landscape Analysis." Data 9, no. 4 (March 31, 2024): 52. http://dx.doi.org/10.3390/data9040052.
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Understanding NLP patents provides valuable insights into innovation trends and competitive dynamics in artificial intelligence. This study uses the Lens patent database to investigate the landscape of NLP patents. The overall patent output in the NLP field on a global scale has exhibited a rapid growth over the past decade, indicating rising research and commercial interests in applying NLP techniques. By analyzing patent assignees, technology categories, and geographic distribution, we identify leading innovators as well as research hotspots in applying NLP. The patent landscape reflects intensifying competition between technology giants and research institutions. This research aims to synthesize key patterns and developments in NLP innovation revealed through patent data analysis, highlighting implications for firms and policymakers. A detailed understanding of NLP patenting activity can inform intellectual property strategy and technology investment decisions in this burgeoning AI domain.
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Sulistyo, Danang, Fadhli Ahda, and Vivi Aida Fitria. "Epistomologi dalam Natural Language Processing." Jurnal Inovasi Teknologi dan Edukasi Teknik 1, no. 9 (September 26, 2021): 652–64. http://dx.doi.org/10.17977/um068v1i92021p652-664.
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How to obtain the truth about knowledge by considering the axiology and anthology aspects of knowledge is the challenge that epistemology must solve. While in scientific epistemology, the accumulation of information that is true will affect how inquiries about the universe are answered heuristically and how natural occurrences are predicted. The primary goal and aim of epistemology, a subfield of philosophy of science, is to investigate and ascertain the nature of knowledge. As such, it examines the origin, sources, and importance of validity from knowledge in addition to discussing the extent and veracity of science. The goal of NLP, a branch of artificial intelligence (AI), is to enable computers to comprehend human language. For instance, text and voice, which people frequently utilize in casual discussions. Integrating computational linguistics with predictive methods led to the development of NLP. NLP has so far done well with text and audio data. There are still others who believe that NLP is in decline, particularly when it comes to managing idioms and sarcasm in contextual data. Due to the vast number of local languages spoken worldwide, the millions of words they contain, the hundreds of regional accents, and their importance in preventing the extinction of local languages, even machine translation, which was the initial purpose of NLP, may still be investigated further.
Masalah yang harus dihadapi oleh Epistomologi adalah bagaimana mendapatkan kebenaran akan pengetahuan dengan menimbang aspek antologi dan aksiologi pada pengetahuan. Sedangkan pada epistomologi ilmiah, penyusunan kebenaran suatu pengetahuan akan berpengaruh untuk menjawab pertanyaan di dunia secara heuristis serta dalam memprediksi fenomena alam yang terjadi. Mempelajari dan menentukan hakikat dari suatu pengetahuan adalah fungsi dan tugas utama epistomologi sebagai salah satu cabang dari filsafat ilmu, maka tidak hanya berbicara tentang kebenaran ilmu pengetahuan dan ruang lingkup pengetahuan, akan tetapi secara luas epistomologi juga mempelajari tentang asal mula, sumber dan juga nilai validitas dari pengetahuan. Pemrosesan bahasa alami, atau NLP, adalah bagian dari kecerdasan buatan (AI) yang berkaitan dengan memberi komputer kemampuan untuk memahami bahasa alami manusia. Misalnya teks dan suara yang sering digunakan manusia dalam percakapan sehari-hari. NLP dibuat dengan menggabungkan linguistik komputasi dengan model statistic. Sampai saat ini NLP memiliki performa yang baik pada data teks dan audio. Namun, masih ada orang yang menilai penurunan dunia NLP, terutama dalam penanganan sarkasme dan idiom dalam data kontekstual. Bahkan terjemahan mesin yang merupakan tujuan awal NLP masih dapat dieksplorasi lebih dalam, karena ada banyak bahasa lokal di dunia, ada jutaan kata, ratusan aksen lokal, dan perannya untuk menyelamatkan Bahasa Lokal dari kepunahan.
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Putri, Nastiti Susetyo Fanany, Prasetya Widiharso, Agung Bella Putra Utama, Maharsa Caraka Shakti, and Urvi Ghosh. "Natural Language Processing in Higher Education." Bulletin of Social Informatics Theory and Application 6, no. 1 (July 3, 2023): 90–101. http://dx.doi.org/10.31763/businta.v6i1.593.
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The application of Natural Language Processing (NLP) in an educational institution is still quite broad in its scope of use, including the use of NLP on chatterbots for academic consultations, handling service dissatisfaction, and spam email detection. Meanwhile, other uses that have not been widely used are the combination of NLP and Global Positioning Satellite (GPS) in finding the location of lecture buildings and other facilities in universities. The combination of NLP and GPS is expected to make it easier for new students, as well as visitors from outside the university, to find the targeted building and facilities more effectively.
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Geetha, Dr V., Dr C. K. Gomathy, Mr P. V. Sri Ram, and Surya Prakash L N. "NOVEL STUDY ON NATURAL LANGUAGE PROCESSING." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 11 (November 1, 2023): 1–11. http://dx.doi.org/10.55041/ijsrem27091.
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Natural Language Processing (NLP) is the tech wiz working tirelessly to break down language barriers between us and our devices. It's the reason our smart phone, tablet or laptop understands our voice commands and translates our languages in a second. NLP is like giving machines the ability to comprehend and respond to language nuances, turning our interactions into seamless conversations. Think of it as the digital polyglot that not only reads but truly understands the messages we convey, from the simplest text to the most intricate emotions. From predictive text to chatbots, NLP is the digital linguist enhancing the way we communicate with our devices, making technology feel more like a conversation with a helpful virtual friend. Keywords: seamless conversations, digital polyglot, predictive text, chatbots, digital linguist, technology communication, virtual friend.
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Zhao, Liping, Waad Alhoshan, Alessio Ferrari, Keletso J. Letsholo, Muideen A. Ajagbe, Erol-Valeriu Chioasca, and Riza T. Batista-Navarro. "Natural Language Processing for Requirements Engineering." ACM Computing Surveys 54, no. 3 (June 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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Dallo, Khan Ali Marwani. "Natural language processing for business analytics." Advances in Engineering Innovation 3, no. 1 (October 23, 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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Trummer, Immanuel. "Database Tuning using Natural Language Processing." ACM SIGMOD Record 50, no. 3 (December 2021): 27–28. http://dx.doi.org/10.1145/3503780.3503788.
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Introduction. We have seen significant advances in the state of the art in natural language processing (NLP) over the past few years [20]. These advances have been driven by new neural network architectures, in particular the Transformer model [19], as well as the successful application of transfer learning approaches to NLP [13]. Typically, training for specific NLP tasks starts from large language models that have been pre-trained on generic tasks (e.g., predicting obfuscated words in text [5]) for which large amounts of training data are available. Using such models as a starting point reduces task-specific training cost as well as the number of required training samples by orders of magnitude [7]. These advances motivate new use cases for NLP methods in the context of databases.
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Patra, Bhupesh, and Mahendra Kumar. "Natural Language Processing in Chatbots: A Review." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 11, no. 3 (December 15, 2020): 2890–94. http://dx.doi.org/10.61841/turcomat.v11i3.14655.
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Natural Language Processing (NLP) plays a critical role in the development of chatbots, enabling them to understand and generate human-like language. This paper provides a comprehensive review of the applications, challenges, and future directions of NLP in chatbots. It discusses the fundamental principles of NLP, including tokenization, part-of-speech tagging, named entity recognition, and sentiment analysis, and examines how these techniques are used in chatbots. The paper also explores the challenges and limitations of NLP in chatbots, such as ambiguity in language, multilingual support, privacy concerns, and integration with existing systems. Additionally, it discusses recent advances in NLP, such as neural language models and transfer learning, and their potential impact on the future development of chatbots. Ethical considerations in NLP development are also addressed. Overall, the paper highlights the significant role of NLP in advancing chatbot technology and the challenges that must be overcome to realize its full potential.
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Panchal, Drashti, Mihika Mehta, Aryaman Mishra, Saish Ghole, and Mrs Smita Dandge. "Sentiment Analysis Using Natural Language Processing." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (May 31, 2022): 2262–66. http://dx.doi.org/10.22214/ijraset.2022.42711.
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Abstract: In recent years, there has been an increasing interest in using natural language processing (NLP) to perform sentiment analysis. This is because NLP can help to automatically extract and identify the sentiment expressed in text data, which is often more accurate and reliable than using human annotation. There are a variety of NLP techniques that can be used for sentiment analysis, including opinion mining, text classification, and lexical analysis. Each of these methods has its own advantages and disadvantages, and the choice of technique will often depend on the type and quality of the text data that is available. In general, sentiment analysis using NLP is a very promising area of research with many potential applications. As more and more text data is generated, it will become increasingly important to be able to automatically extract the sentiment expressed in this data.
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Sun, Tian-Xiang, Xiang-Yang Liu, Xi-Peng Qiu, and Xuan-Jing Huang. "Paradigm Shift in Natural Language Processing." Machine Intelligence Research 19, no. 3 (May 28, 2022): 169–83. http://dx.doi.org/10.1007/s11633-022-1331-6.
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AbstractIn the era of deep learning, modeling for most natural language processing (NLP) tasks has converged into several mainstream paradigms. For example, we usually adopt the sequence labeling paradigm to solve a bundle of tasks such as POS-tagging, named entity recognition (NER), and chunking, and adopt the classification paradigm to solve tasks like sentiment analysis. With the rapid progress of pre-trained language models, recent years have witnessed a rising trend of paradigm shift, which is solving one NLP task in a new paradigm by reformulating the task. The paradigm shift has achieved great success on many tasks and is becoming a promising way to improve model performance. Moreover, some of these paradigms have shown great potential to unify a large number of NLP tasks, making it possible to build a single model to handle diverse tasks. In this paper, we review such phenomenon of paradigm shifts in recent years, highlighting several paradigms that have the potential to solve different NLP tasks.
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Wang, Yi. "Prediction and processing of natural language." Applied and Computational Engineering 4, no. 1 (June 14, 2023): 215–24. http://dx.doi.org/10.54254/2755-2721/4/20230454.
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With the rapid increase in the computing power of electronic computers and the dramatic decrease in the cost of manufacturing, researchers are refocusing on the challenging research field of natural language processing (NLP). Therefore, in a natural language training development as the main line, from the following several aspects work: first of all, on the basis of preliminary training technical update route, this paper introduces the traditional natural language training in advance technology and training neural network prediction technology, and analyse the related technical features, comparison, generalize the development and trends of natural language processing technology; Secondly, this paper introduces the improved natural language processing models based on BERT from two aspects, and summarizes these models from the aspects of pre-training mechanism, advantages and disadvantages, and performance. Furthermore, the main application fields of NLP are introduced, and the current challenges and corresponding solutions of NLP are described. Finally, this paper summarizes the work and predict the future development direction of NLP.
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Bachate, Ravindra Parshuram, and Ashok Sharma. "Acquaintance with Natural Language Processing for Building Smart Society." E3S Web of Conferences 170 (2020): 02006. http://dx.doi.org/10.1051/e3sconf/202017002006.
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Natural Language Processing (NLP) deals with the spoken languages by using computer and Artificial Intelligence. As people from different regional areas using different digital platforms and expressing their views in their spoken language, it is now must to focus on working spoken languages in India to make our society smart and digital. NLP research grown tremendously in last decade which results in Siri, Google Assistant, Alexa, Cortona and many more automatic speech recognitions and understanding systems (ASR). Natural Language Processing can be understood by classifying it into Natural Language Generation and Natural Language Understanding. NLP is widely used in various domain such as Health Care, Chatbot, ASR building, HR, Sentiment analysis etc.
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Wen, Bo. "Research on the applications of natural language processing." Applied and Computational Engineering 16, no. 1 (October 23, 2023): 220–27. http://dx.doi.org/10.54254/2755-2721/16/20230896.
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In recent years, there has been a rapid advancement in natural language processing (NLP), leading to notable improvements in areas like sentiment analysis, machine translation, and text recognition. However, belonging to the same field under AI, the research materials and academic topics of NLP are not so adequate. Therefore, this paper introduces and shows the reader a general introduction to NLP for the current research environment, so that the reader can clearly understand the history of NLP, its uses, and the current and future research directions. It also analyzes the current mainstream applications of NLP, including the most basic Rule Based method and the more popular deep learning, development of pre-trained language models such as GPT. Besides, this article also analyzes the current mainstream application scenarios of NLP and its related use of technology, so that readers can have a clearer picture of the research direction of NLP. At the end, this paper also summarizes the general development of NLP, research methods, and application scenarios to provides an outlook on the future development of NLP with respect to these points.
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Raghav, Mayank. "NATURAL LANGUAGE PROCESSING BASED THREAT ASSESSMENT." International Journal of Engineering Applied Sciences and Technology 8, no. 6 (October 1, 2023): 94–99. http://dx.doi.org/10.33564/ijeast.2023.v08i06.012.
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Military intelligence agencies are continually faced with the formidable task of evaluating and mitigating diverse risks in an increasingly complex and dynamic global landscape. NLP has emerged as a powerful tool for enhancing the capabilities of Military Intelligence operations. NLP techniques enable the automatic extraction, analysis, and interpretation of information from vast volumes of unstructured textual data, such as open-source intelligence reports, social media, intercepted communications. By processing and understanding these textual sources, NLP systems can assist Military Intelligence analysts in identifying potential threats, uncovering hidden patterns, and making timely and informed decisions. Threat assessment is required to safeguard security interests and maintain the sovereignty of nation. By the rapidly expanding fields in emerging technologies India can gain that decisive advantage.
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Rumaisa, Fitrah, Yan Puspitarani, Ai Rosita, Azizah Zakiah, and Sriyani Violina. "Penerapan Natural Language Processing (NLP) di bidang pendidikan." Jurnal Inovasi Masyarakat 1, no. 3 (December 20, 2021): 232–35. http://dx.doi.org/10.33197/jim.vol1.iss3.2021.799.
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NLP adalah cabang dari kecerdasan buatan (AI) yang berhubungan dengan melatih komputer untuk memahami, memproses, dan menghasilkan bahasa. Salah satu implementasi NLP yang sangat penting adalah penerapannya di dunia pendidikan. NLP adalah proses yang efektif untuk membantu siswa dalam proses pembelajaran. Menerapkan NLP dalam lingkungan pendidikan tidak hanya membantu dalam mengembangkan proses bahasa yang efektif, tetapi juga penting untuk meningkatkan prestasi akademik. Beberapa penerapan NLP di dunia pendidikan adalah Peringkasan Teks dan Paraphrasing, Tanya Jawab, Chatbot (feedback dari pendidik), Evaluasi Ejaan dan Grammar
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Patki, Medha, and Pranja Soni. "Natural language processing: Understanding the current landscape." MIT Science Policy Review 3 (August 29, 2022): 152–57. http://dx.doi.org/10.38105/spr.pvh55o2k8e.
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We spoke with two researchers in Natural Language Processing (NLP) to understand their perspective on the current landscape of NLP - the challenges, successes, and implications of the technology today.
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K, Sharavana, Kedarnath Bhakta, Jayanth Sai Chethan S, Jayant Chand, and Meet Joshi K. "Evolution of Natural Language Processing: A Review." Journal of Knowledge in Data Science and Information Management 1, no. 1 (April 15, 2024): 30–38. http://dx.doi.org/10.46610/jokdsim.2024.v01i01.004.
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Over the years, Natural Language Processing (NLP) has evolved dramatically, moving from early rule based systems to the current era dominated by advanced deep learning models. An overview of the significant turning points and patterns that have influenced the development of NLP is given in this study. In the early days of Natural Language Processing (NLP), rule based methods were the main focus. Linguists would manually create rules to analyze and comprehend human language. Although these systems were somewhat successful, they were unable to handle the complexity and unpredictability of spoken language. A major change was brought about by the introduction of probabilistic models and machine learning techniques with the emergence of statistical approaches. The development of methods like n gram models and hidden Markov models during this time allowed computers to handle linguistic patterns. Large scale linguistic resources like word embeddings and annotated corpora started to appear, which further accelerated the development of NLP. Machine learning algorithms have led to notable advancements in tasks such as machine translation, named entity recognition, and part of speech tagging. NLP has seen a revolution in recent years thanks to deep learning, which uses neural networks to learn intricate language representations. Sequential dependencies in language can now be better understood because of models like Long Short Term Memory Networks (LSTMs) and Recurrent Neural Networks (RNNs). The addition of attention mechanisms, as demonstrated by Transformer and other models, improves the model's ability to manage long range dependencies and perform better on a variety of NLP tasks. In the future, NLP will develop in ways that go beyond performance measurements, exploring interpretability, ethical issues, and the incorporation of multimodal data. As the area develops, it becomes increasingly important to eliminate biases, ensure ethical AI deployment, and improve user centric experiences. This introduction lays the groundwork for an in depth examination of the development of NLP, highlighting significant turning points, difficulties, and potential future directions in this vibrant and quickly developing subject.
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Tushar Agarwal, Jitender Jangid, Gaurav Kumar. "Transformer and Natural language processing; A recent development." Tuijin Jishu/Journal of Propulsion Technology 44, no. 1 (November 24, 2023): 140–43. http://dx.doi.org/10.52783/tjjpt.v44.i1.2225.
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The convergence of transformers and natural language processing (NLP) represents a watershed moment in the realm of artificial intelligence. Recent NLP advancements have been profoundly shaped by the emergence of transformers, a class of deep learning models renowned for their remarkable proficiency in comprehending, generating, and manipulating human language. This abstract offers a succinct exploration of the symbiotic relationship between transformers and NLP, emphasizing their central role in propelling recent process.
The introduction of transformers heralded a paradigm shift in the realm of NLP, primarily owing to their innovative self-attention mechanism, which empowers them to adeptly capture intricate contextual associations within textual data. Distinguished by their multi-head attention layers and feed-forward networks, the architecture of transformers has ushered in a new era in NLP. Models such as BERT,
GPT-3, and their offshoots have not only redefined but also set the gold standard for a wide array of NLP tasks.
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Geetha, Dr V., Dr C. K. Gomathy, Mr D. Sri Datta Vallab Yaratha Yagn, and Sai Praneesh. "THE ROLE OF NATURAL LANGUAGE PROCESSING." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 11 (November 1, 2023): 1–11. http://dx.doi.org/10.55041/ijsrem27094.
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Natural Language Processing (NLP) is a rapidly evolving field in the intersection of computer science, artificial intelligence, and linguistics. This article provides an overview of NLP, tracing its historical development from early rule-based systems to contemporary deep learning models. Natural Language Processing is a subfield of computer science and artificial intelligence that deals with the interactions between computers and humans using natural language. It focuses on the ability of computers to understand, interpret, and generate human language. Keywords Natural Language Processing, Text Analysis , Text Mining , Speech Recognition.
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Krishna, Guntamukkala Gopi. "Multilingual NLP." International Journal of Advanced Engineering and Nano Technology 10, no. 6 (June 30, 2023): 9–12. http://dx.doi.org/10.35940/ijaent.e4119.0610623.
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The subject area of multilingual natural language processing (NLP) is concerned with the processing of natural language data in several languages. NLP systems that can translate between languages are becoming more and more necessary as the globe gets more interconnected in order to promote understanding and communication among speakers of various languages. To be effective, communication must overcome a number of obstacles presented by multilingual NLP. Lack of language standardization, which results in major variations in the grammatical constructions, vocabulary, and writing systems used in many languages, is one of the fundamental problems. The requirement for substantial amounts of annotated data for machine learning model training presents another difficulty. The creation of high-quality annotated datasets in numerous languages is time- and money-consuming, which restricts the supply of multilingual NLP resources. The problem of creating NLP systems that can handle several languages at once is the last one. This necessitates the deployment of sophisticated algorithms that can handle and evaluate data in numerous languages while producing precise findings. Researchers and developers are working on a variety of methods to address these issues. Creating standardized formats for multilingual data representation, like Universal Dependencies, which offers a unified framework for annotating linguistic data in several languages, is one strategy. Using transfer learning techniques to transfer knowledge from high-resource languages to low-resource languages is an alternative strategy. The amount of annotated data required for training NLP models in low-resource languages can bede creased with the use of this method. Last but not least, researchers are working to create multilingual NLP models that can manage numerous languages at once. To deliver precise results across numerous languages, these models employ cutting-edge methodologies like neural machine translation and multilingual word embedding’s. Despite the fact that multilingual NLP presents a number of difficult issues, with continuing study and development, it is possible to create NLP systems that are capable of processing natural language data from several languages.
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Hutchinson, Tim. "Natural language processing and machine learning as practical toolsets for archival processing." Records Management Journal 30, no. 2 (May 16, 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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Seol, Hee Yun, Mary C. Rolfes, Wi Chung, Sunghwan Sohn, Euijung Ryu, Miguel A. Park, Hirohito Kita, et al. "Expert artificial intelligence-based natural language processing characterises childhood asthma." BMJ Open Respiratory Research 7, no. 1 (February 2020): e000524. http://dx.doi.org/10.1136/bmjresp-2019-000524.
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IntroductionThe lack of effective, consistent, reproducible and efficient asthma ascertainment methods results in inconsistent asthma cohorts and study results for clinical trials or other studies. We aimed to assess whether application of expert artificial intelligence (AI)-based natural language processing (NLP) algorithms for two existing asthma criteria to electronic health records of a paediatric population systematically identifies childhood asthma and its subgroups with distinctive characteristics.MethodsUsing the 1997–2007 Olmsted County Birth Cohort, we applied validated NLP algorithms for Predetermined Asthma Criteria (NLP-PAC) as well as Asthma Predictive Index (NLP-API). We categorised subjects into four groups (both criteria positive (NLP-PAC+/NLP-API+); PAC positive only (NLP-PAC+ only); API positive only (NLP-API+ only); and both criteria negative (NLP-PAC−/NLP-API−)) and characterised them. Results were replicated in unsupervised cluster analysis for asthmatics and a random sample of 300 children using laboratory and pulmonary function tests (PFTs).ResultsOf the 8196 subjects (51% male, 80% white), we identified 1614 (20%), NLP-PAC+/NLP-API+; 954 (12%), NLP-PAC+ only; 105 (1%), NLP-API+ only; and 5523 (67%), NLP-PAC−/NLP-API−. Asthmatic children classified as NLP-PAC+/NLP-API+ showed earlier onset asthma, more Th2-high profile, poorer lung function, higher asthma exacerbation and higher risk of asthma-associated comorbidities compared with other groups. These results were consistent with those based on unsupervised cluster analysis and lab and PFT data of a random sample of study subjects.ConclusionExpert AI-based NLP algorithms for two asthma criteria systematically identify childhood asthma with distinctive characteristics. This approach may improve precision, reproducibility, consistency and efficiency of large-scale clinical studies for asthma and enable population management.
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Guez Dellove, Ondongo Aucibi Adrard, and Kamalaraj R. "Natural Language Processing (NLP) in Recommendation Systems." International Journal of Innovative Research in Computer and Communication Engineering 12, no. 05 (May 17, 2024): 5974–76. http://dx.doi.org/10.15680/ijircce.2024.1205140.
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This paper delves into the application of Natural Language Processing (NLP) techniques in recommendation systems, specifically focusing on novel approaches to enhance recommendation accuracy and user satisfaction. The utilization of NLP algorithms has revolutionized how content is recommended to users, leveraging linguistic analysis and machine learning to understand user preferences and provide tailored suggestions. Our research explores various NLP methodologies, including sentiment analysis, topic modelling, and semantic analysis, to extract meaningful insights from textual data. Furthermore, we investigate the integration of deep learning models, such as neural networks and transformer architectures, to capture complex patterns and improve recommendation precision. A key highlight of our study is the introduction of a novel recommendation method termed "Knowledge Graph Embedding for Contextual Recommendation." This innovative approach combines knowledge graph representation with contextual understanding, allowing for more nuanced and personalized recommendations based on user interactions, historical data, and contextual relevance. We delve into the intricacies of this technique, detailing its implementation, training process, and evaluation metrics.
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Bharadiya, Jasmin. "Transfer Learning in Natural Language Processing (NLP)." European Journal of Technology 7, no. 2 (June 5, 2023): 26–35. http://dx.doi.org/10.47672/ejt.1490.
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Purpose: The purpose of this study is to address the limited use of transfer learning techniques in radio frequency machine learning and to propose a customized taxonomy for radio frequency applications. The aim is to enable performance gains, improved generalization, and cost-effective training data solutions in this specific domain.
Methodology: The research design employed in this study involves a comprehensive review of existing literature on transfer learning in radio frequency machine learning. The researchers collected relevant papers from reputable sources and analyzed them to identify patterns, trends, and insights. The method of data collection primarily relied on examining and synthesizing existing literature. Data analysis involved identifying key findings and developing a customized taxonomy for radio frequency applications.
Findings: The study's findings highlight the limited utilization of transfer learning techniques in radio frequency machine learning. While transfer learning has shown significant performance improvements in computer vision and natural language processing, its potential in the wireless communications domain has yet to be fully explored. The customized taxonomy proposed in this study provides a consistent framework for analyzing and comparing existing and future efforts in this field.
Recommendations: Based on the findings, the study recommends further research and experimentation to explore the potential of transfer learning techniques in radio frequency machine learning. This includes investigating performance gains, improving generalization capabilities, and addressing concerns related to training data costs. Additionally, collaborations between researchers and practitioners in the field are encouraged to facilitate knowledge exchange and foster innovation. Practice: To practitioners in the field of radio frequency machine learning, this study emphasizes the potential benefits of incorporating transfer learning techniques. It encourages practitioners to explore the application of transfer learning in their specific domain, leveraging prior knowledge to enhance performance and address training data challenges. It also highlights the importance of staying informed about the latest developments and collaborating with experts in the field. Policy: To policy makers, the study underscores the need for supportive policies that promote research and development in radio frequency machine learning. It recommends creating an environment that fosters innovation, encourages collaborations between academia and industry, and provides resources and incentives for further exploration of transfer learning techniques. Policy makers should consider the potential impact of transfer learning on the wireless communications industry and support initiatives that enhance its adoption and implementation.
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YIKUN, LI. "Review on natural language processing models." Applied and Computational Engineering 35, no. 1 (January 22, 2024): 1–7. http://dx.doi.org/10.54254/2755-2721/35/20230350.
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Accessing information has grown simpler as a result of the internet's expanding use and the arrival of the big data era. Compared to traditional approaches, employing NLP for information condensation and amalgamation proves to be a highly effective method. This article focuses primarily on the sentiment analysis aspect of NLP, offering a comprehensive exploration of two deep learning models: BERT and CNN. It delves into the intricacies of their principles, analyzes their respective strengths and weaknesses, and proposes potential avenues for enhancement. By delving into these models, Researchers and practitioners can obtain a better understanding of sentiment analysis and its applications in diverse fields.
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Diab, Kareem Mahmoud, Jamie Deng, Yusen Wu, Yelena Yesha, Fernando Collado-Mesa, and Phuong Nguyen. "Natural Language Processing for Breast Imaging: A Systematic Review." Diagnostics 13, no. 8 (April 14, 2023): 1420. http://dx.doi.org/10.3390/diagnostics13081420.
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Natural Language Processing (NLP) has gained prominence in diagnostic radiology, offering a promising tool for improving breast imaging triage, diagnosis, lesion characterization, and treatment management in breast cancer and other breast diseases. This review provides a comprehensive overview of recent advances in NLP for breast imaging, covering the main techniques and applications in this field. Specifically, we discuss various NLP methods used to extract relevant information from clinical notes, radiology reports, and pathology reports and their potential impact on the accuracy and efficiency of breast imaging. In addition, we reviewed the state-of-the-art in NLP-based decision support systems for breast imaging, highlighting the challenges and opportunities of NLP applications for breast imaging in the future. Overall, this review underscores the potential of NLP in enhancing breast imaging care and offers insights for clinicians and researchers interested in this exciting and rapidly evolving field.
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Deck, Oliver. "Bullshit, Pragmatic Deception, and Natural Language Processing." Dialogue & Discourse 14, no. 1 (May 24, 2023): 56–87. http://dx.doi.org/10.5210/dad.2023.103.
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Fact checking and fake news detection has garnered increasing interest within the natural language processing (NLP) community in recent years, yet other aspects of misinformation remain unexplored. One such phenomenon is `bullshit', which different disciplines have tried to define since it first entered academic discussion nearly four decades ago. Fact checking bullshitters is useless, because factual reality typically plays no part in their assertions: Where liars deceive about content, bullshitters deceive about their goals. Bullshitting is misleading about language itself, which necessitates identifying the points at which pragmatic conventions are broken with deceptive intent. This paper aims to introduce bullshitology into the field of NLP by tying it to questions in a QUD-based definition, providing two approaches to bullshit annotation, and finally outlining which combinations of NLP methods will be helpful to classify which kinds of linguistic bullshit.
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Zhu, Qingqing. "Natural Language Processing in Teacher Training : a systematic review." Lecture Notes in Education Psychology and Public Media 18, no. 1 (October 26, 2023): 83–90. http://dx.doi.org/10.54254/2753-7048/18/20231293.
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In the previous decade, there has been a growing interest within the research community to apply artificial intelligence (AI), particularly natural language processing (NLP) tech-nology, across various domains such as law, medicine, and finance. More recently, the fo-cus has shifted towards exploring the potential of NLP technology in education, specifi-cally in teacher training. Thus, it becomes crucial to conduct a systematic literature review to comprehensively examine the literature on the use of NLP technology in teacher train-ing. This study concentrates on the applications and use cases of NLP technology in higher education institutions and educational research institutions. Our analysis suggests that significant NLP applications in education include Language Learning, intelligent analysis, assistive technology, automatic content analysis, and speech emotion analysis. Further examination reveals that NLP technology can be effectively utilized to improve teachers professional abilities, such as helping language teachers improve their accents, ultimately contributing to the delivery of high-quality education. Finally, this paper summarizes the critical lessons learned from the application of NLP technology in teacher training that can guide future research endeavors in this rapidly evolving field.
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Gu, Yu, Robert Tinn, Hao Cheng, Michael Lucas, Naoto Usuyama, Xiaodong Liu, Tristan Naumann, Jianfeng Gao, and Hoifung Poon. "Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing." ACM Transactions on Computing for Healthcare 3, no. 1 (January 31, 2022): 1–23. http://dx.doi.org/10.1145/3458754.
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Pretraining large neural language models, such as BERT, has led to impressive gains on many natural language processing (NLP) tasks. However, most pretraining efforts focus on general domain corpora, such as newswire and Web. A prevailing assumption is that even domain-specific pretraining can benefit by starting from general-domain language models. In this article, we challenge this assumption by showing that for domains with abundant unlabeled text, such as biomedicine, pretraining language models from scratch results in substantial gains over continual pretraining of general-domain language models. To facilitate this investigation, we compile a comprehensive biomedical NLP benchmark from publicly available datasets. Our experiments show that domain-specific pretraining serves as a solid foundation for a wide range of biomedical NLP tasks, leading to new state-of-the-art results across the board. Further, in conducting a thorough evaluation of modeling choices, both for pretraining and task-specific fine-tuning, we discover that some common practices are unnecessary with BERT models, such as using complex tagging schemes in named entity recognition. To help accelerate research in biomedical NLP, we have released our state-of-the-art pretrained and task-specific models for the community, and created a leaderboard featuring our BLURB benchmark (short for Biomedical Language Understanding & Reasoning Benchmark) at https://aka.ms/BLURB .
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Belov, Serey, Daria Zrelova, Petr Zrelov, and Vladimir Korenkov. "Overview of methods for automatic natural language text processing." System Analysis in Science and Education, no. 3 (2020) (September 30, 2020): 8–22. http://dx.doi.org/10.37005/2071-9612-2020-3-8-22.
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This paper provides a brief overview of modern methods and approaches used for automatic processing of text information. In English-language literature, this area of science is called NLP-Natural Language Processing. The very name suggests that the subject of analysis (and for many tasks – and synthesis) are materials presented in one of the natural languages (and for a number of tasks – in several languages simultaneously), i.e. national languages of communication between people. Programming languages are not included in this group. In Russian-language literature, this area is called Computer (or mathematical) linguistics. NLP (computational linguistics) usually includes speech analysis along with text analysis, but in this review speech analysis does not consider. The review used materials from original works, monographs, and a number of articles published the «Open Systems.DBMS» journal.
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Treviso, Marcos, Ji-Ung Lee, Tianchu Ji, Betty van Aken, Qingqing Cao, Manuel R. Ciosici, Michael Hassid, et al. "Efficient Methods for Natural Language Processing: A Survey." Transactions of the Association for Computational Linguistics 11 (2023): 826–60. http://dx.doi.org/10.1162/tacl_a_00577.
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Abstract Recent work in natural language processing (NLP) has yielded appealing results from scaling model parameters and training data; however, using only scale to improve performance means that resource consumption also grows. Such resources include data, time, storage, or energy, all of which are naturally limited and unevenly distributed. This motivates research into efficient methods that require fewer resources to achieve similar results. This survey synthesizes and relates current methods and findings in efficient NLP. We aim to provide both guidance for conducting NLP under limited resources, and point towards promising research directions for developing more efficient methods.
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Abhishek Pandey, V Ramesh, Puneet Mittal, Suruthi, Muniyandy Elangovan, and G.Deepa. "Exploring advancements in deep learning for natural language processing tasks." Scientific Temper 14, no. 04 (December 31, 2023): 1316–23. http://dx.doi.org/10.58414/scientifictemper.2023.14.4.38.
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This literature survey explores the transformative influence of deep learning on Natural Language Processing (NLP), revealing a dynamic interplay between these fields. Deep learning techniques, characterized by neural network architectures, have propelled NLP into a realm where machines not only comprehend but also generate human language. The survey covers various NLP applications, such as sentiment analysis, machine translation, text summarization, question answering, and speech recognition, scasing significant strides attributed to deep learning models like Transformer, BERT, GPT, and attention-based Sequence-to-Sequence models. These advancements have redefined the landscape of NLP tasks, setting new benchmarks for performance. ever, challenges persist, including limited data availability in certain languages, increasing model sizes, and ethical considerations related to bias and fairness. Overcoming these hurdles requires innovative approaches for data scarcity, the development of computationally efficient models, and a focus on ethical practices in research and application. This survey provides a comprehensive overview of the progress and obstacles in integrating deep learning with NLP, offering a roadmap for navigating this evolving domain.
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Shaitarova, Anastassia, Jamil Zaghir, Alberto Lavelli, Michael Krauthammer, and Fabio Rinaldi. "Exploring the Latest Highlights in Medical Natural Language Processing across Multiple Languages: A Survey." Yearbook of Medical Informatics 32, no. 01 (August 2023): 230–43. http://dx.doi.org/10.1055/s-0043-1768726.
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Objectives: This survey aims to provide an overview of the current state of biomedical and clinical Natural Language Processing (NLP) research and practice in Languages other than English (LoE). We pay special attention to data resources, language models, and popular NLP downstream tasks. Methods: We explore the literature on clinical and biomedical NLP from the years 2020-2022, focusing on the challenges of multilinguality and LoE. We query online databases and manually select relevant publications. We also use recent NLP review papers to identify the possible information lacunae. Results: Our work confirms the recent trend towards the use of transformer-based language models for a variety of NLP tasks in medical domains. In addition, there has been an increase in the availability of annotated datasets for clinical NLP in LoE, particularly in European languages such as Spanish, German and French. Common NLP tasks addressed in medical NLP research in LoE include information extraction, named entity recognition, normalization, linking, and negation detection. However, there is still a need for the development of annotated datasets and models specifically tailored to the unique characteristics and challenges of medical text in some of these languages, especially low-resources ones. Lastly, this survey highlights the progress of medical NLP in LoE, and helps at identifying opportunities for future research and development in this field.
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Xiao, Yijun, and William Yang Wang. "Quantifying Uncertainties in Natural Language Processing Tasks." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 7322–29. http://dx.doi.org/10.1609/aaai.v33i01.33017322.
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Reliable uncertainty quantification is a first step towards building explainable, transparent, and accountable artificial intelligent systems. Recent progress in Bayesian deep learning has made such quantification realizable. In this paper, we propose novel methods to study the benefits of characterizing model and data uncertainties for natural language processing (NLP) tasks. With empirical experiments on sentiment analysis, named entity recognition, and language modeling using convolutional and recurrent neural network models, we show that explicitly modeling uncertainties is not only necessary to measure output confidence levels, but also useful at enhancing model performances in various NLP tasks.
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Young, Marcus, Natasha Holmes, Raymond Robbins, Nada Marhoon, Sobia Amjad, Ary Serpa Neto, and Rinaldo Bellomo. "Natural language processing to assess the epidemiology of delirium-suggestive behavioural disturbances in critically ill patients." Critical Care and Resuscitation 23, no. 2 (June 7, 2021): 144–53. http://dx.doi.org/10.51893/2021.2.oa1.
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Background: There is no gold standard approach for delirium diagnosis, making the assessment of its epidemiology difficult. Delirium can only be inferred though observation of behavioural disturbance and described with relevant nouns or adjectives. Objective: We aimed to use natural language processing (NLP) and its identification of words descriptive of behavioural disturbance to study the epidemiology of delirium in critically ill patients. Study design: Retrospective study using data collected from the electronic health records of a university-affiliated intensive care unit (ICU) in Melbourne, Australia. Participants: 12 375 patients Intervention: Analysis of electronic progress notes. Identification using NLP of at least one of a list of words describing behavioural disturbance within such notes. Results: We analysed 199 648 progress notes in 12 375 patients. Of these, 5108 patients (41.3%) had NLP-diagnosed behavioural disturbance (NLP-Dx-BD). Compared with those who did not have NLP-Dx-DB, these patients were older, more severely ill, and likely to have medical or unplanned admissions, neurological diagnosis, chronic kidney or liver disease and to receive mechanical ventilation and renal replacement therapy (P < 0.001). The unadjusted hospital mortality for NLP-Dx-BD patients was 14.1% versus 9.6% for patients without NLP-Dx-BD. After adjustment for baseline characteristics and illness severity, NLP-Dx-BD was not associated with increased risk of death (odds ratio [OR], 0.94; 95% CI, 0.80–1.10); a finding robust to multiple sensitivity, subgroups and time of observation subcohort analyses. In mechanically ventilated patients, NLP-Dx-BD was associated with decreased hospital mortality (OR, 0.80; 95% CI, 0.65–0.99) after adjustment for baseline severity of illness and year of admission. Conclusions: NLP enabled rapid assessment of large amounts of data identifying a population of ICU patients with typical high risk characteristics for delirium. Moreover, this technique enabled identification of previously poorly understood associations. Further investigations of this technique appear justified.
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Simorangkir, Anastasya, Putri Intani Sihite, Cindi Lusia Kiareni, Ressa Priskila, and Viktor Handrianus Pranatawijaya. "PEMODELAN CHATBOT REKOMENDASI HOTEL DENGAN MENGGUNAKAN NATURAL LANGUAGE PROCESSING." Simtek : jurnal sistem informasi dan teknik komputer 9, no. 1 (April 29, 2024): 46–50. http://dx.doi.org/10.51876/simtek.v9i1.371.
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Chatbot merupakan program yang dirancang untuk berinteraksi dengan manusia melalui percakapan berbasis teks atau suara. Sebagai tren yang semakin populer, chatbot telah digunakan dalam berbagai bidang kehidupan, termasuk ketika kita sedang mencari rekomendasi hotel. Proses pencarian hotel secara tradisional seringkali memakan waktu dan membingungkan, mendorong pengguna untuk mencari alternatif yang lebih efisien. Dalam konteks ini, chatbot dengan Natural Language Processing (NLP) menawarkan solusi yang potensial. Penelitian ini bertujuan untuk mengembangkan model chatbot untuk membantu mencari rekomendasi hotel dengan menggunakan metode NLP. Pengumpulan data dilakukan melalui analisis kebutuhan pengguna. Selanjutnya, sistem chatbot dirancang dan diintegrasikan dengan sistem rekomendasi hotel yang ada. Tahap berikutnya adalah pelatihan model NLP menggunakan data teks dari percakapan rekomendasi yang ada dan yang tidak ada dalam database untuk melihat respon. Kinerja chatbot dievaluasi berdasarkan akurasi dalam memahami permintaan pengguna, efisiensi dalam memberikan respons. Hasil penelitian menunjukkan bahwa penggunaan NLP dalam chatbot dapat mengatasi beberapa masalah dalam proses mencari rekomendasi hotel, dengan chatbot NLP cenderung lebih akurat, cepat dan efisien.
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Abram, Marissa D., Karen T. Mancini, and R. David Parker. "Methods to Integrate Natural Language Processing Into Qualitative Research." International Journal of Qualitative Methods 19 (January 1, 2020): 160940692098460. http://dx.doi.org/10.1177/1609406920984608.
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Background: Qualitative methods analyze contextualized, unstructured data. These methods are time and cost intensive, often resulting in small sample sizes and yielding findings that are complicated to replicate. Integrating natural language processing (NLP) into a qualitative project can increase efficiency through time and cost savings; increase sample sizes; and allow for validation through replication. This study compared the findings, costs, and time spent between a traditional qualitative method (Investigator only) to a method pairing a qualitative investigator with an NLP function (Investigator +NLP). Methods: Using secondary data from a previously published study, the investigators designed an NLP process in Python to yield a corpus, keywords, keyword influence, and the primary topics. A qualitative researcher reviewed and interpreted the output. These findings were compared to the previous study results. Results: Using comparative review, our results closely matched the original findings. The NLP + Investigator method reduced the project time by a minimum of 120 hours and costs by $1,500. Discussion: Qualitative research can evolve by incorporating NLP methods. These methods can increase sample size, reduce project time, and significantly reduce costs. The results of an integrated NLP process create a corpus and code which can be reviewed and verified, thus allowing a replicable, qualitative study. New data can be added over time and analyzed using the same interpretation and identification. Off the shelf qualitative software may be easier to use, but it can be expensive and may not offer a tailored approach or easily interpretable outcomes which further benefits researchers.
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Chang, Kuei-Hu. "Natural Language Processing: Recent Development and Applications." Applied Sciences 13, no. 20 (October 17, 2023): 11395. http://dx.doi.org/10.3390/app132011395.
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Sharma, Nakul, and Prasanth Yalla. "Developing Research Questions in Natural Language Processing and Software Engineering." JOIV : International Journal on Informatics Visualization 2, no. 4 (August 7, 2018): 268. http://dx.doi.org/10.30630/joiv.2.4.159.
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This paper endeavors to develop newer medium of developing research questions by keeping in view both fields of SE and NLP in proper perspectives. An overview of the current state of art research in SE and NLP is presented. This is done by referring to the SE Body of Knowledge (SEBOK). Analogues to SEBOK, there are no separate Body of Knowledge available for the NLP/Computational Linguistics (CL). Hence whatever falls within the category of NLP/CL was considered in framing the research categories from the NLP/CL side. The paper concludes with future scope of the research presented.
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S., Panchal, and Thakur P. "HARNESSING THE POWER OF NATURAL LANGUAGE PROCESSING IN NURSING SERVICES." International Journal of Advanced Research 12, no. 05 (May 31, 2024): 154–56. http://dx.doi.org/10.21474/ijar01/18697.
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Natural Language Processing (NLP) has emerged as a transformative force in healthcare, revolutionizing various aspects of nursing services. This research article explores the applications, benefits, challenges, and future directions of NLP in nursing. Through a comprehensive review, this article highlights the pivotal role of NLP in clinical decision support, health records management, patient interaction, and research synthesis. Real-world case studies illustrate the tangible impact of NLP on improving efficiency, accuracy, and patient outcomes. While acknowledging the advantages of NLP, the article also addresses challenges such as data privacy, model bias, and implementation hurdles. Future predictions envision NLPs evolution towards personalized care, precision medicine, and population health management. The article concludes with a call to action for healthcare organizations to invest in NLP research and collaboration, paving the way for a brighter healthcare future.
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Ananth Rao, Ananya, and Prof Venkatesh S. "Identification of Aphasia using Natural Language Processing." Journal of University of Shanghai for Science and Technology 23, no. 06 (June 28, 2021): 1737–47. http://dx.doi.org/10.51201/jusst/21/06488.
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Aphasia is a neurological disorder of language that precludes a person’s ability to speak, understand, read or write in any language. By virtue of this disorder being inextricably connected to language, there is a vast potential for the application of Natural Language Processing (NLP) for the diagnosis of the disorder. This paper surveys the automated machine-learning-based classification methodologies followed by an attempt to discuss a potential way in which an NLP-backed methodology could be implemented along with its accompanying challenges. It is seen that the need for standardized technology-based diagnostic solutions necessitates the exploration of such a methodology.
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Guarasci, Raffaele, Giuseppe De Pietro, and Massimo Esposito. "Quantum Natural Language Processing: Challenges and Opportunities." Applied Sciences 12, no. 11 (June 2, 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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Aigerim, K. A. "МОДЕЛИ ОБРАБОТКИ ЕСТЕСТВЕННОГО ЯЗЫКА ДЛЯ УЛУЧШЕНИЯ СЕМАНТИЧЕСКИХ РЕЗУЛЬТАТОВ ПОИСКА." INTERNATIONAL JOURNAL OF INFORMATION AND COMMUNICATION TECHNOLOGIES 3, no. 2(10) (June 15, 2022): 82–91. http://dx.doi.org/10.54309/ijict.2022.10.2.008.
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Most people rely on search engines to get and share information from all sorts of resources. All totals returned by search engines are not always important because they are drawn from heterogeneous data sources. Moreover, it is not easy for a user to prove that the acquired results are relevant for the request. As a result, the semantic network plays a significant role in interpreting the relevance of search results. This paper proposes a new method for searching for appropriate documents using the semantic web, based on the concept of natural language processing (NLP). In the proposed system, NLP is used to analyze a user request in terms of parts of speech. The extracted definitions are compared with a domain dictionary to identify the appropriate domain of the user's interest. The retrieved user request papers are examined with natural language processing support to identify the respective domain. Keywords: semantic search engine, natural language processing, context analysis, information retrieval systems, graphematic analysis, ontology
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Dhyani, Bijesh. "Transfer Learning in Natural Language Processing: A Survey." Mathematical Statistician and Engineering Applications 70, no. 1 (January 31, 2021): 303–11. http://dx.doi.org/10.17762/msea.v70i1.2312.
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ABSTRACT
Transfer learning is a discipline that is expanding quickly within the realm of natural language processing (NLP) and machine learning. It is the application of previously learned models to the solution of a variety of problems that are connected to one another. This paper presents a comprehensive survey of transfer learning techniques in NLP, focusing on five key classification algorithms: (1) BERT, (2) GPT, (3) ELMo, (4) RoBERTa, and (5) ALBERT. We discuss the fundamental concepts, methodologies, and performance benchmarks of each algorithm, highlighting the various approaches taken to leverage pre-existing knowledge for effective learning. Furthermore, we provide an overview of the latest advancements and challenges in transfer learning for NLP, along with promising directions for future research in this domain.
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Taskin, Zehra, and Umut Al. "Natural language processing applications in library and information science." Online Information Review 43, no. 4 (August 12, 2019): 676–90. http://dx.doi.org/10.1108/oir-07-2018-0217.
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Purpose With the recent developments in information technologies, natural language processing (NLP) practices have made tasks in many areas easier and more practical. Nowadays, especially when big data are used in most research, NLP provides fast and easy methods for processing these data. The purpose of this paper is to identify subfields of library and information science (LIS) where NLP can be used and to provide a guide based on bibliometrics and social network analyses for researchers who intend to study this subject. Design/methodology/approach Within the scope of this study, 6,607 publications, including NLP methods published in the field of LIS, are examined and visualized by social network analysis methods. Findings After evaluating the obtained results, the subject categories of publications, frequently used keywords in these publications and the relationships between these words are revealed. Finally, the core journals and articles are classified thematically for researchers working in the field of LIS and planning to apply NLP in their research. Originality/value The results of this paper draw a general framework for LIS field and guides researchers on new techniques that may be useful in the field.