Journal articles: 'Natural language processing (Computer science) Artificial intelligence'

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Suzuki, Kenji. "AI: A New Open Access Journal for Artificial Intelligence." AI 1, no. 2 (March 26, 2020): 141–42. http://dx.doi.org/10.3390/ai1020007.

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As a branch of computer science, artificial intelligence (AI) attempts to understand the essence of intelligence, and produce new kinds of intelligent machines that can respond in a similar way to human intelligence, with broad research areas of machine and deep learning, data science, reinforcement learning, data mining, knowledge discovery, knowledge reasoning, speech recognition, natural language processing, language recognition, image recognition, computer vision, planning, robotics, gaming, and so on [...]

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Bajwa, Imran Sarwar. "Virtual Telemedicine Using Natural Language Processing." International Journal of Information Technology and Web Engineering 5, no. 1 (January 2010): 43–55. http://dx.doi.org/10.4018/jitwe.2010010103.

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Conventional telemedicine has limitations due to the existing time constraints in the response of a medical specialist. One major reason is that telemedicine based medical facilities are subject to the availability of a medical expert and telecommunication facilities. On the other hand, communication using telecommunication is only possible on fixed and appointed time. Typically, the field of telemedicine exists in both medical and telecommunication areas to provide medical facilities over a long distance, especially in remote areas. In this article, the authors present a solution for ‘virtual telemedicine’ to cope with the problem of the long time constraints in conventional telemedicine. Virtual Telemedicine is the use of telemedicine with the methods of artificial intelligence.

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Bailin, Alan. "Artificial Intelligence and Computer-Assisted Language Instruction: A Perspective." CALICO Journal 5, no. 3 (January 14, 2013): 25–45. http://dx.doi.org/10.1558/cj.v5i3.25-45.

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The article attempts to outline the major components of CALI-AI (computer-assisted language instruction incorporating artificial intelligence techniques). The article begins by discussing briefly the central assumption on which CALI-AI work is based, that is, that human cognitive abilities can be reproduced by mechanical means. It then proceeds to examine the following components of CALI-AI: (1) natural language processing, problem solving, (3) language learning, and (4) modeling teacher behavior. The article concludes with a discussion of the ways in which language teachers can participate in the development of the field.

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Montejo-Ráez, Arturo, and Salud María Jiménez-Zafra. "Current Approaches and Applications in Natural Language Processing." Applied Sciences 12, no. 10 (May 11, 2022): 4859. http://dx.doi.org/10.3390/app12104859.

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Sokolov, I. A. "Theory and practice in artificial intelligence." Вестник Российской академии наук 89, no. 4 (April 24, 2019): 365–70. http://dx.doi.org/10.31857/s0869-5873894365-370.

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Artificial Intelligence is an interdisciplinary field, and formed about 60 years ago as an interaction between mathematical methods, computer science, psychology, and linguistics. Artificial Intelligence is an experimental science and today features a number of internally designed theoretical methods: knowledge representation, modeling of reasoning and behavior, textual analysis, and data mining. Within the framework of Artificial Intelligence, novel scientific domains have arisen: non-monotonic logic, description logic, heuristic programming, expert systems, and knowledge-based software engineering. Increasing interest in Artificial Intelligence in recent years is related to the development of promising new technologies based on specific methods like knowledge discovery (or machine learning), natural language processing, autonomous unmanned intelligent systems, and hybrid human-machine intelligence.

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Long, Teng, Zhangbing Zhou, Gerhard Hancke, Yang Bai, and Qi Gao. "A Review of Artificial Intelligence Technologies in Mineral Identification: Classification and Visualization." Journal of Sensor and Actuator Networks 11, no. 3 (August 29, 2022): 50. http://dx.doi.org/10.3390/jsan11030050.

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Artificial intelligence is a branch of computer science that attempts to understand the essence of intelligence and produce a new intelligent machine capable of responding in a manner similar to human intelligence. Research in this area includes robotics, language recognition, image identification, natural language processing, and expert systems. In recent years, the availability of large datasets, the development of effective algorithms, and access to powerful computers have led to unprecedented success in artificial intelligence. This powerful tool has been used in numerous scientific and engineering fields including mineral identification. This paper summarizes the methods and techniques of artificial intelligence applied to intelligent mineral identification based on research, classifying the methods and techniques as artificial neural networks, machine learning, and deep learning. On this basis, visualization analysis is conducted for mineral identification of artificial intelligence from field development paths, research hot spots, and keywords detection, respectively. In the end, based on trend analysis and keyword analysis, we propose possible future research directions for intelligent mineral identification.

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Yang, Junpu. "Research on Security Model Design Based on Computational Network and Natural Language Processing." Mobile Information Systems 2022 (August 31, 2022): 1–14. http://dx.doi.org/10.1155/2022/7191312.

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Human logical thinking exists in the form of language, and most of the knowledge is also recorded and transmitted in the form of language. It is also an important and even core part of artificial intelligence. Communicating with computers in natural language is a long-standing pursuit of people. People can use the computer in the language they are most accustomed to and can also use it to learn more about human language abilities and intelligent mechanisms. The realization of natural language communication between humans and computers means that computers can not only understand the meaning of natural language texts but also express the intentions and thoughts given in natural language texts. This paper designs and studies a computational model for natural language processing (NLP) models for natural language processing. This paper aims to study the design of computing network security model based on natural language processing. This paper proposes three calculation models, which are based on the long-term and short-term memory neural network model (LSTM), FastText model, and text processing model (GCN) based on graph convolution neural network. Several natural language processing models are evaluated and analyzed using four indexes: accuracy, recall, exactness, and F1 vaule. Results show that the performance level of the GCN model is the best. The accuracy of the NLP recognition of this model reaches 86.66%, which is 2.93% and 1.55% higher than the accuracy of the LSTM model and the FastText model, respectively.

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Kim, Jin-Dong. "Biomedical Natural Language Processing." Computational Linguistics 43, no. 1 (April 2017): 265–67. http://dx.doi.org/10.1162/coli_r_00281.

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Esposito, Massimo, Giovanni Luca Masala, Aniello Minutolo, and Marco Pota. "Special Issue on “Natural Language Processing: Emerging Neural Approaches and Applications”." Applied Sciences 11, no. 15 (July 22, 2021): 6717. http://dx.doi.org/10.3390/app11156717.

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Islam, Saiful. "Artificial Intelligence in Healthcare." International Journal of Engineering Materials and Manufacture 6, no. 4 (October 1, 2021): 319–23. http://dx.doi.org/10.26776/ijemm.06.04.2021.08.

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Artificial intelligence (AI) is the ability of a computer program or machine to think or learn that possess human-like intelligence. These computing devices use this intelligence to provide services such as speech recognition, natural language processing and identifying disease in healthcare. To work efficiently, AI requires adequate data that is used to train systems. The efficiency of any AI system depends on the availability of this data.Â This article is mainly focused on recent advents in the technology of Artificial Intelligence. The importance of AI in healthcare is identified and described in this report. The applications of Artificial Intelligence in healthcare such as clinical care, medical research, drug research and public healthcare are briefly discussed here. The purpose of this article is to demonstrate that artificial intelligence is being used in all domains of life and particularly in the field of healthcare. This report presents the role of Artificial Intelligence in healthcare.

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Dou, Zi-Yi, Xing Wang, Shuming Shi, and Zhaopeng Tu. "Exploiting deep representations for natural language processing." Neurocomputing 386 (April 2020): 1–7. http://dx.doi.org/10.1016/j.neucom.2019.12.060.

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Brown, Laura E., and David Kauchak. "Educational Advances in Artificial Intelligence." AI Magazine 34, no. 4 (September 11, 2013): 127. http://dx.doi.org/10.1609/aimag.v34i4.2508.

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The emergence of massive open online courses has initiated a broad national-wide discussion on higher education practices, models, and pedagogy. Artificial intelligence and machine learning courses were at the forefront of this trend and are also being used to serve personalized, managed content in the back-end systems. Massive open online courses are just one example of the sorts of pedagogical innovations being developed to better teach AI. This column will discuss and share innovative educational approaches that teach or leverage AI and its many subfields, including robotics, machine learning, natural language processing, computer vision, and others at all levels of education (K-12, undergraduate, and graduate levels). In particular, this column will serve the community as a venue to learn about the Symposium on Educational Advances in Artificial Intelligence (EAAI) (colocated with AAAI for the past four years); introductions to innovative pedagogy and best practices for AI and across the computer science curricula; resources for teaching AI, including model AI assignments, software packages, online videos and lectures that can be used in your classroom; topic tutorials introducing a subject to students and researchers with links to articles, presentations, and online materials; and discussion of the use of AI methods in education shaping personalized tutorials, learning analytics, and data mining

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Palade, Vasile, and J. Gerard Wolff. "A Roadmap for the Development of the ‘SP Machine’ for Artificial Intelligence." Computer Journal 62, no. 11 (January 22, 2019): 1584–604. http://dx.doi.org/10.1093/comjnl/bxy126.

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AbstractThis paper describes a roadmap for the development of the SP Machine, based on the SP Theory of Intelligence and its realization in the SP Computer Model. The SP Machine will be developed initially as a software virtual machine with high levels of parallel processing, hosted on a high-performance computer. The system should help users visualize knowledge structures and processing. Research is needed into how the system may discover low-level features in speech and in images. Strengths of the SP System in the processing of natural language may be augmented, in conjunction with the further development of the SP System’s strengths in unsupervised learning. Strengths of the SP System in pattern recognition may be developed for computer vision. Work is needed on the representation of numbers and the performance of arithmetic processes. A computer model is needed of SP-Neural, the version of the SP Theory expressed in terms of neurons and their interconnections. The SP Machine has potential in many areas of application, several of which may be realized on short-to-medium timescales.

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Widdows, Dominic, Kirsty Kitto, and Trevor Cohen. "Quantum Mathematics in Artificial Intelligence." Journal of Artificial Intelligence Research 72 (December 14, 2021): 1307–41. http://dx.doi.org/10.1613/jair.1.12702.

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In the decade since 2010, successes in artificial intelligence have been at the forefront of computer science and technology, and vector space models have solidified a position at the forefront of artificial intelligence. At the same time, quantum computers have become much more powerful, and announcements of major advances are frequently in the news. The mathematical techniques underlying both these areas have more in common than is sometimes realized. Vector spaces took a position at the axiomatic heart of quantum mechanics in the 1930s, and this adoption was a key motivation for the derivation of logic and probability from the linear geometry of vector spaces. Quantum interactions between particles are modelled using the tensor product, which is also used to express objects and operations in artificial neural networks. This paper describes some of these common mathematical areas, including examples of how they are used in artificial intelligence (AI), particularly in automated reasoning and natural language processing (NLP). Techniques discussed include vector spaces, scalar products, subspaces and implication, orthogonal projection and negation, dual vectors, density matrices, positive operators, and tensor products. Application areas include information retrieval, categorization and implication, modelling word-senses and disambiguation, inference in knowledge bases, decision making, and and semantic composition. Some of these approaches can potentially be implemented on quantum hardware. Many of the practical steps in this implementation are in early stages, and some are already realized. Explaining some of the common mathematical tools can help researchers in both AI and quantum computing further exploit these overlaps, recognizing and exploring new directions along the way.This paper describes some of these common mathematical areas, including examples of how they are used in artificial intelligence (AI), particularly in automated reasoning and natural language processing (NLP). Techniques discussed include vector spaces, scalar products, subspaces and implication, orthogonal projection and negation, dual vectors, density matrices, positive operators, and tensor products. Application areas include information retrieval, categorization and implication, modelling word-senses and disambiguation, inference in knowledge bases, and semantic composition. Some of these approaches can potentially be implemented on quantum hardware. Many of the practical steps in this implementation are in early stages, and some are already realized. Explaining some of the common mathematical tools can help researchers in both AI and quantum computing further exploit these overlaps, recognizing and exploring new directions along the way.

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Zhang, Wei Emma, Quan Z. Sheng, Ahoud Alhazmi, and Chenliang Li. "Adversarial Attacks on Deep-learning Models in Natural Language Processing." ACM Transactions on Intelligent Systems and Technology 11, no. 3 (May 13, 2020): 1–41. http://dx.doi.org/10.1145/3374217.

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Dale, Robert, Hermann Moisl, and Harold Somers. "Handbook of Natural Language Processing." Computational Linguistics 27, no. 4 (December 2001): 602–3. http://dx.doi.org/10.1162/coli.2000.27.4.602.

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Kak, Subhash C. "The Paninian approach to natural language processing." International Journal of Approximate Reasoning 1, no. 1 (January 1987): 117–30. http://dx.doi.org/10.1016/0888-613x(87)90007-7.

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Kanaparthi, Vijaya. "Examining Natural Language Processing Techniques in the Education and Healthcare Fields." International Journal of Engineering and Advanced Technology 12, no. 2 (December 30, 2022): 8–18. http://dx.doi.org/10.35940/ijeat.b3861.1212222.

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Natural language processing is a branch of artificial intelligence currently being used to classify unstructured data. While natural language processing is found throughout several fields, these algorithms are currently being excelled in the education and healthcare fields. The healthcare industry has found various uses of natural language processing models. These algorithms are capable of analyzing large amounts of unstructured data from clinical notes, making it easier for healthcare professionals to identify at-risk patients and analyze consumer healthcare perception. In the education field, researchers are utilizing natural language processing models to enhance student academic success, reading comprehension, and to evaluate the fairness of student evaluations. Both fields have been able to find use of natural language model processing models. Some business leaders, however, are fearful of natural language processing. This review seeks to explore the various uses of natural language processing in the healthcare and education fields to determine the benefit and disadvantages these models have on both fields.

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Wang, Haoran, Yue Zhang, and Xiaosheng Yu. "An Overview of Image Caption Generation Methods." Computational Intelligence and Neuroscience 2020 (January 9, 2020): 1–13. http://dx.doi.org/10.1155/2020/3062706.

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In recent years, with the rapid development of artificial intelligence, image caption has gradually attracted the attention of many researchers in the field of artificial intelligence and has become an interesting and arduous task. Image caption, automatically generating natural language descriptions according to the content observed in an image, is an important part of scene understanding, which combines the knowledge of computer vision and natural language processing. The application of image caption is extensive and significant, for example, the realization of human-computer interaction. This paper summarizes the related methods and focuses on the attention mechanism, which plays an important role in computer vision and is recently widely used in image caption generation tasks. Furthermore, the advantages and the shortcomings of these methods are discussed, providing the commonly used datasets and evaluation criteria in this field. Finally, this paper highlights some open challenges in the image caption task.

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da Rocha, Naila Camila, Abner Macola Pacheco Barbosa, Yaron Oliveira Schnr, Juliana Machado-Rugolo, Luis Gustavo Modelli de Andrade, José Eduardo Corrente, and Liciana Vaz de Arruda Silveira. "Natural Language Processing to Extract Information from Portuguese-Language Medical Records." Data 8, no. 1 (December 29, 2022): 11. http://dx.doi.org/10.3390/data8010011.

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Studies that use medical records are often impeded due to the information presented in narrative fields. However, recent studies have used artificial intelligence to extract and process secondary health data from electronic medical records. The aim of this study was to develop a neural network that uses data from unstructured medical records to capture information regarding symptoms, diagnoses, medications, conditions, exams, and treatment. Data from 30,000 medical records of patients hospitalized in the Clinical Hospital of the Botucatu Medical School (HCFMB), São Paulo, Brazil, were obtained, creating a corpus with 1200 clinical texts. A natural language algorithm for text extraction and convolutional neural networks for pattern recognition were used to evaluate the model with goodness-of-fit indices. The results showed good accuracy, considering the complexity of the model, with an F-score of 63.9% and a precision of 72.7%. The patient condition class reached a precision of 90.3% and the medication class reached 87.5%. The proposed neural network will facilitate the detection of relationships between diseases and symptoms and prevalence and incidence, in addition to detecting the identification of clinical conditions, disease evolution, and the effects of prescribed medications.

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Bhattamisra, Subrat Kumar, Priyanka Banerjee, Pratibha Gupta, Jayashree Mayuren, Susmita Patra, and Mayuren Candasamy. "Artificial Intelligence in Pharmaceutical and Healthcare Research." Big Data and Cognitive Computing 7, no. 1 (January 11, 2023): 10. http://dx.doi.org/10.3390/bdcc7010010.

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Artificial intelligence (AI) is a branch of computer science that allows machines to work efficiently, can analyze complex data. The research focused on AI has increased tremendously, and its role in healthcare service and research is emerging at a greater pace. This review elaborates on the opportunities and challenges of AI in healthcare and pharmaceutical research. The literature was collected from domains such as PubMed, Science Direct and Google scholar using specific keywords and phrases such as ‘Artificial intelligence’, ‘Pharmaceutical research’, ‘drug discovery’, ‘clinical trial’, ‘disease diagnosis’, etc. to select the research and review articles published within the last five years. The application of AI in disease diagnosis, digital therapy, personalized treatment, drug discovery and forecasting epidemics or pandemics was extensively reviewed in this article. Deep learning and neural networks are the most used AI technologies; Bayesian nonparametric models are the potential technologies for clinical trial design; natural language processing and wearable devices are used in patient identification and clinical trial monitoring. Deep learning and neural networks were applied in predicting the outbreak of seasonal influenza, Zika, Ebola, Tuberculosis and COVID-19. With the advancement of AI technologies, the scientific community may witness rapid and cost-effective healthcare and pharmaceutical research as well as provide improved service to the general public.

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Sarma, Jhumpa. "Role of Artificial Intelligence in Medicine and Clinical Research." International Journal for Research in Applied Science and Engineering Technology 9, no. 8 (August 31, 2021): 1512–18. http://dx.doi.org/10.22214/ijraset.2021.37617.

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Abstract: Artificial Intelligence is a branch of computer science that enables to analyse complex medical data. The proficiency of artificial intelligence techniques has been explored to a great extent in the field of medicine. Most of the medications go to the business sector after a long tedious process of drug development. It can take a period of 10-15 years or more to convey a medication from its introductory revelation to the hands of the patients. Artificial Intelligence can significantly reduce the time required and can also cut down the expenses by half. Among the methods, artificial neural network is the most widely used analytical tool while other techniques like fuzzy expert systems, natural language processing, robotic process automation and evolutionary computation have been used in different clinical settings. The aim of this paper is to discuss the different artificial intelligence techniques and provide a perspective on the benefits, future opportunities and risks of established artificial intelligence applications in clinical practice on medical education, physicians, healthcare institutions and bioethics. Keywords: Artificial intelligence, clinical trials, medical technologies, artificial neural networks, diagnosis.

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Bird, Steven. "Natural Language Processing and Linguistic Fieldwork." Computational Linguistics 35, no. 3 (September 2009): 469–74. http://dx.doi.org/10.1162/coli.35.3.469.

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Louis, Annie. "Natural Language Processing for Social Media." Computational Linguistics 42, no. 4 (December 2016): 833–36. http://dx.doi.org/10.1162/coli_r_00270.

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Duh, Kevin. "Bayesian Analysis in Natural Language Processing." Computational Linguistics 44, no. 1 (March 2018): 187–89. http://dx.doi.org/10.1162/coli_r_00310.

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Yang, Baosong, Longyue Wang, Derek F. Wong, Shuming Shi, and Zhaopeng Tu. "Context-aware Self-Attention Networks for Natural Language Processing." Neurocomputing 458 (October 2021): 157–69. http://dx.doi.org/10.1016/j.neucom.2021.06.009.

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Sangers, Jordy, Flavius Frasincar, Frederik Hogenboom, and Vadim Chepegin. "Semantic Web service discovery using natural language processing techniques." Expert Systems with Applications 40, no. 11 (September 2013): 4660–71. http://dx.doi.org/10.1016/j.eswa.2013.02.011.

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Russo, Antonia, and Gianluca Lax. "Using Artificial Intelligence for Space Challenges: A Survey." Applied Sciences 12, no. 10 (May 19, 2022): 5106. http://dx.doi.org/10.3390/app12105106.

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Artificial intelligence is applied to many fields and contributes to many important applications and research areas, such as intelligent data processing, natural language processing, autonomous vehicles, and robots. The adoption of artificial intelligence in several fields has been the subject of many research papers. Still, recently, the space sector is a field where artificial intelligence is receiving significant attention. This paper aims to survey the most relevant problems in the field of space applications solved by artificial intelligence techniques. We focus on applications related to mission design, space exploration, and Earth observation, and we provide a taxonomy of the current challenges. Moreover, we present and discuss current solutions proposed for each challenge to allow researchers to identify and compare the state of the art in this context.

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Cambria, Erik, and Bebo White. "Jumping NLP Curves: A Review of Natural Language Processing Research [Review Article]." IEEE Computational Intelligence Magazine 9, no. 2 (May 2014): 48–57. http://dx.doi.org/10.1109/mci.2014.2307227.

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Huang, Jian, Jing Li, Zheming Li, Zhu Zhu, Chen Shen, Guoqiang Qi, and Gang Yu. "Detection of Diseases Using Machine Learning Image Recognition Technology in Artificial Intelligence." Computational Intelligence and Neuroscience 2022 (April 13, 2022): 1–14. http://dx.doi.org/10.1155/2022/5658641.

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With the continuous development and improvement of artificial intelligence technology, machine learning technology has also been extensively developed, which has promoted the development of computer vision, image processing, natural language processing, and other fields. Purpose. This article aims to apply the image processing technology based on machine learning in the detection of childhood diseases and propose the application of image processing technology to the detection of childhood diseases. This article introduces machine learning, image recognition technology, and related algorithms in detail and experiments on image recognition technology based on machine learning. The experimental results show that image recognition technology based on machine learning can well identify white blood cells that are difficult to distinguish with the naked eye, with a recognition rate of up to 90%. Applying image recognition technology based on machine learning in disease diagnosis has greatly improved the level of medical diagnosis.

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ŠALOUN, Petr, Barbora CIGÁNKOVÁ, David ANDREŠIČ, and Lenka KRHUTOVÁ. "SUPPORT OF INFORMAL CARERS FOR PEOPLE AFTER A STROKE WITH CROWDSOURCING AND NATURAL LANGUAGE PROCESSING." Acta Electrotechnica et Informatica 21, no. 3 (December 20, 2021): 3–10. http://dx.doi.org/10.15546/aeei-2021-0013.

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For a long time, both professionals and the lay public showed little interest in informal carers. Yet these people deals with multiple and common issues in their everyday lives. As the population is aging we can observe a change of this attitude. And thanks to the advances in computer science, we can offer them some effective assistance and support by providing necessary information and connecting them with both professional and lay public community. In this work we describe a project called “Research and development of support networks and information systems for informal carers for persons after stroke” producing an information system visible to public as a web portal. It does not provide just simple a set of information but using means of artificial intelligence, text document classification and crowdsourcing further improving its accuracy, it also provides means of effective visualization and navigation over the content made by most by the community itself and personalized on a level of informal carer’s phase of the care-taking timeline. In can be beneficial for informal carers as it allows to find a content specific to their current situation. This work describes our approach to classification of text documents and its improvement through crowdsourcing. Its goal is to test text documents classifier based on documents similarity measured by N-grams method and to design evaluation and crowdsourcing-based classification improvement mechanism. Interface for crowdsourcing was created using CMS WordPress. In addition to data collection, the purpose of interface is to evaluate classification accuracy, which leads to extension of classifier test data set, thus the classification is more successful.

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Chrupała, Grzegorz. "Visually Grounded Models of Spoken Language: A Survey of Datasets, Architectures and Evaluation Techniques." Journal of Artificial Intelligence Research 73 (February 18, 2022): 673–707. http://dx.doi.org/10.1613/jair.1.12967.

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This survey provides an overview of the evolution of visually grounded models of spoken language over the last 20 years. Such models are inspired by the observation that when children pick up a language, they rely on a wide range of indirect and noisy clues, crucially including signals from the visual modality co-occurring with spoken utterances. Several fields have made important contributions to this approach to modeling or mimicking the process of learning language: Machine Learning, Natural Language and Speech Processing, Computer Vision and Cognitive Science. The current paper brings together these contributions in order to provide a useful introduction and overview for practitioners in all these areas. We discuss the central research questions addressed, the timeline of developments, and the datasets which enabled much of this work. We then summarize the main modeling architectures and offer an exhaustive overview of the evaluation metrics and analysis techniques.

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Cao, Zehong. "A review of artificial intelligence for EEG‐based brain−computer interfaces and applications." Brain Science Advances 6, no. 3 (September 2020): 162–70. http://dx.doi.org/10.26599/bsa.2020.9050017.

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The advancement in neuroscience and computer science promotes the ability of the human brain to communicate and interact with the environment, making brain–computer interface (BCI) top interdisciplinary research. Furthermore, with the modern technology advancement in artificial intelligence (AI), including machine learning (ML) and deep learning (DL) methods, there is vast growing interest in the electroencephalogram (EEG)‐based BCIs for AI‐related visual, literal, and motion applications. In this review study, the literature on mainstreams of AI for the EEG‐based BCI applications is investigated to fill gaps in the interdisciplinary BCI field. Specifically, the EEG signals and their main applications in BCI are first briefly introduced. Next, the latest AI technologies, including the ML and DL models, are presented to monitor and feedback human cognitive states. Finally, some BCI‐inspired AI applications, including computer vision, natural language processing, and robotic control applications, are presented. The future research directions of the EEG‐based BCI are highlighted in line with the AI technologies and applications.

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Zanzotto, Fabio Massimo, Giorgio Satta, and Giordano Cristini. "CYK Parsing over Distributed Representations." Algorithms 13, no. 10 (October 15, 2020): 262. http://dx.doi.org/10.3390/a13100262.

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Parsing is a key task in computer science, with applications in compilers, natural language processing, syntactic pattern matching, and formal language theory. With the recent development of deep learning techniques, several artificial intelligence applications, especially in natural language processing, have combined traditional parsing methods with neural networks to drive the search in the parsing space, resulting in hybrid architectures using both symbolic and distributed representations. In this article, we show that existing symbolic parsing algorithms for context-free languages can cross the border and be entirely formulated over distributed representations. To this end, we introduce a version of the traditional Cocke–Younger–Kasami (CYK) algorithm, called distributed (D)-CYK, which is entirely defined over distributed representations. D-CYK uses matrix multiplication on real number matrices of a size independent of the length of the input string. These operations are compatible with recurrent neural networks. Preliminary experiments show that D-CYK approximates the original CYK algorithm. By showing that CYK can be entirely performed on distributed representations, we open the way to the definition of recurrent layer neural networks that can process general context-free languages.

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Armstrong, Susan, Kenneth Church, Pierre Isabelle, Sandra Manzi, Evelyne Tzoukermann, and David Yarowsky. "Natural Language Processing Using Very Large Corpora." Computational Linguistics 26, no. 2 (June 2000): 294. http://dx.doi.org/10.1162/coli.2000.26.2.294a.

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Liu, Yang, and Meng Zhang. "Neural Network Methods for Natural Language Processing." Computational Linguistics 44, no. 1 (March 2018): 193–95. http://dx.doi.org/10.1162/coli_r_00312.

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Zhang, Nan, Jingfeng Xue, Yuxi Ma, Ruyun Zhang, Tiancai Liang, and Yu‐an Tan. "Hybrid sequence‐based Android malware detection using natural language processing." International Journal of Intelligent Systems 36, no. 10 (July 12, 2021): 5770–84. http://dx.doi.org/10.1002/int.22529.

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Mikelionienė, Jurgita, and Jurgita Motiejūnienė. "Corpus-based analysis of semi-automatically extracted artificial intelligence-related terminology." Journal of Language and Cultural Education 9, no. 1 (March 1, 2021): 30–38. http://dx.doi.org/10.2478/jolace-2021-0003.

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Abstract Artificial Intelligence (AI), as a multidisciplinary field, combines computer science, robotics and cognitive science, with increasingly growing applications in many diverse areas, such as engineering, business, medicine, weather forecasting, industry, translation, natural language, linguistics, etc. In Europe, interest in AI has been rising in the last decade. One of the greatest hurdles for researchers in automated processing of technical documentation is large amounts of specific terminology. The aim of this research is to analyse the semi-automatically extracted artificial intelligence-related terminology and the most common phrases related to artificial intelligence in English and Lithuanian in terms of their structure, multidisciplinarity and connotation. For selection and analysis of terms, two programmes were chosen in this study, namely SynchroTerm and SketchEngine. The paper presents the outcomes of an AI terminological project carried out with SynchroTerm and provides an analysis of a special corpus compiled in the field of artificial intelligence using the SketchEngine platform. The analysis of semi-automatic term extraction use and corpus-based techniques for artificial intelligence-related terminology revealed that AI as a specialized domain contains multidisciplinary terminology, and is complex and dynamic. The empiric data shows that the context is essential for the evaluation of the concept under analysis and reveals the different connotation of the term.

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El Morr, Christo, Pierre Maret, Fabrice Muhlenbach, Dhayananth Dharmalingam, Rediet Tadesse, Alexandra Creighton, Bushra Kundi, et al. "A Virtual Community for Disability Advocacy: Development of a Searchable Artificial Intelligence–Supported Platform." JMIR Formative Research 5, no. 11 (November 5, 2021): e33335. http://dx.doi.org/10.2196/33335.

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Background The lack of availability of disability data has been identified as a major challenge hindering continuous disability equity monitoring. It is important to develop a platform that enables searching for disability data to expose systemic discrimination and social exclusion, which increase vulnerability to inequitable social conditions. Objective Our project aims to create an accessible and multilingual pilot disability website that structures and integrates data about people with disabilities and provides data for national and international disability advocacy communities. The platform will be endowed with a document upload function with hybrid (automated and manual) paragraph tagging, while the querying function will involve an intelligent natural language search in the supported languages. Methods We have designed and implemented a virtual community platform using Wikibase, Semantic Web, machine learning, and web programming tools to enable disability communities to upload and search for disability documents. The platform data model is based on an ontology we have designed following the United Nations Convention on the Rights of Persons with Disabilities (CRPD). The virtual community facilitates the uploading and sharing of validated information, and supports disability rights advocacy by enabling dissemination of knowledge. Results Using health informatics and artificial intelligence techniques (namely Semantic Web, machine learning, and natural language processing techniques), we were able to develop a pilot virtual community that supports disability rights advocacy by facilitating uploading, sharing, and accessing disability data. The system consists of a website on top of a Wikibase (a Semantic Web–based datastore). The virtual community accepts 4 types of users: information producers, information consumers, validators, and administrators. The virtual community enables the uploading of documents, semiautomatic tagging of their paragraphs with meaningful keywords, and validation of the process before uploading the data to the disability Wikibase. Once uploaded, public users (information consumers) can perform a semantic search using an intelligent and multilingual search engine (QAnswer). Further enhancements of the platform are planned. Conclusions The platform ontology is flexible and can accommodate advocacy reports and disability policy and legislation from specific jurisdictions, which can be accessed in relation to the CRPD articles. The platform ontology can be expanded to fit international contexts. The virtual community supports information upload and search. Semiautomatic tagging and intelligent multilingual semantic search using natural language are enabled using artificial intelligence techniques, namely Semantic Web, machine learning, and natural language processing.

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Zheng, Zihui. "Logical Intelligent Detection Algorithm of Chinese Language Articles Based on Text Mining." Mobile Information Systems 2021 (December 16, 2021): 1–10. http://dx.doi.org/10.1155/2021/8115551.

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With the advent of the big data era and the rapid development of the Internet industry, the information processing technology of text mining has become an indispensable role in natural language processing. In our daily life, many things cannot be separated from natural language processing technology, such as machine translation, intelligent response, and semantic search. At the same time, with the development of artificial intelligence, text mining technology has gradually developed into a research hotspot. There are many ways to realize text mining. This paper mainly describes the realization of web text mining and the realization of text structure algorithm based on HTML through a variety of methods to compare the specific clustering time of web text mining. Through this comparison, we can also get which web mining is the most efficient. The use of WebKB datasets for many times in experimental comparison also reflects that Web text mining for the Chinese language logic intelligent detection algorithm provides a basis.

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Qiu, Shilin, Qihe Liu, Shijie Zhou, and Wen Huang. "Adversarial attack and defense technologies in natural language processing: A survey." Neurocomputing 492 (July 2022): 278–307. http://dx.doi.org/10.1016/j.neucom.2022.04.020.

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Sidorov, Grigori, Francisco Velasquez, Efstathios Stamatatos, Alexander Gelbukh, and Liliana Chanona-Hernández. "Syntactic N-grams as machine learning features for natural language processing." Expert Systems with Applications 41, no. 3 (February 2014): 853–60. http://dx.doi.org/10.1016/j.eswa.2013.08.015.

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Fernandes, Marta B., Navid Valizadeh, Haitham S. Alabsi, Syed A. Quadri, Ryan A. Tesh, Abigail A. Bucklin, Haoqi Sun, et al. "Classification of neurologic outcomes from medical notes using natural language processing." Expert Systems with Applications 214 (March 2023): 119171. http://dx.doi.org/10.1016/j.eswa.2022.119171.

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Tafazoli, Dara, Elena Gómez María, and Cristina A. Huertas Abril. "Intelligent Language Tutoring System." International Journal of Information and Communication Technology Education 15, no. 3 (July 2019): 60–74. http://dx.doi.org/10.4018/ijicte.2019070105.

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Intelligent computer-assisted language learning (ICALL) is a multidisciplinary area of research that combines natural language processing (NLP), intelligent tutoring system (ITS), second language acquisition (SLA), and foreign language teaching and learning (FLTL). Intelligent tutoring systems (ITS) are able to provide a personalized approach to learning by assuming the role of a real teacher/expert who adapts and steers the learning process according to the specific needs of each learner. This article reviews and discusses the issues surrounding the development and use of ITSs for language learning and teaching. First, the authors look at ICALL history: its evolution from CALL. Second, issues in ICALL research and integration will be discussed. Third, they will explain how artificial intelligence (AI) techniques are being implemented in language education as ITS and intelligent language tutoring systems (ITLS). Finally, the successful integration and development of ITLS will be explained in detail.

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Mah, Pascal Muam, Iwona Skalna, and John Muzam. "Natural Language Processing and Artificial Intelligence for Enterprise Management in the Era of Industry 4.0." Applied Sciences 12, no. 18 (September 14, 2022): 9207. http://dx.doi.org/10.3390/app12189207.

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Introduction: The advances in the digital era have necessitated the adoption of communication as the main channel for modern business. In the past, business negotiations, profiling, seminars, shopping, and agreements were in-person but today everything is almost digitalized. Objectives: The study aims to examine how the Internet of things (IoTs) connects text-object as part of NLP and AI responding to human needs. Also, how precipitated changes in the business environment and modern applications such as NLP and AI embedded with IoTs services have changed business settings. Problem statement: As communication takes lead in the business environment, companies have developed sophisticated applications of NLP that take human desires and fulfill them instantly with the help of text, phone calls, smart records, and chatbots. The ease of communication and interaction has shown a greater influence on customer choice, desires, and needs. Modern service providers now use email, text, phone calls, smart records, and virtual assistants as first contact points for almost all of their dealings, customer inquiries, and most preferred trading channels. Method: The study uses text content as part of NLP and AI to demonstrate how companies capture customers’ insight and how they use IoTs to influence customers’ reactions, responses, and engagement with enterprise management in Industry 4.0. The “Behavior-oriented drive and influential function of IoTs on Customers in Industry 4.0” concept was used in this study to determine the influence of Industry 4.0 on customers. Results: The result indicates the least score of 12 out of 15 grades for all the measurements on a behavior-oriented drive and influential function of IoTs on customers. Conclusion: The study concluded that NLP and AI are the preferred system for enterprise management in the era of Industry 4.0 to understand customers’ demands and achieve customer satisfaction. Therefore, NLP and AI techniques are a necessity to attain business goals.

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Deng, Ran, and Fedor Duzhin. "Topological Data Analysis Helps to Improve Accuracy of Deep Learning Models for Fake News Detection Trained on Very Small Training Sets." Big Data and Cognitive Computing 6, no. 3 (July 5, 2022): 74. http://dx.doi.org/10.3390/bdcc6030074.

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Topological data analysis has recently found applications in various areas of science, such as computer vision and understanding of protein folding. However, applications of topological data analysis to natural language processing remain under-researched. This study applies topological data analysis to a particular natural language processing task: fake news detection. We have found that deep learning models are more accurate in this task than topological data analysis. However, assembling a deep learning model with topological data analysis significantly improves the model’s accuracy if the available training set is very small.

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Ghosal, Tirthankar, Tanik Saikh, Tameesh Biswas, Asif Ekbal, and Pushpak Bhattacharyya. "Novelty Detection: A Perspective from Natural Language Processing." Computational Linguistics 48, no. 1 (2022): 77–117. http://dx.doi.org/10.1162/coli_a_00429.

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Abstract The quest for new information is an inborn human trait and has always been quintessential for human survival and progress. Novelty drives curiosity, which in turn drives innovation. In Natural Language Processing (NLP), Novelty Detection refers to finding text that has some new information to offer with respect to whatever is earlier seen or known. With the exponential growth of information all across the Web, there is an accompanying menace of redundancy. A considerable portion of the Web contents are duplicates, and we need efficient mechanisms to retain new information and filter out redundant information. However, detecting redundancy at the semantic level and identifying novel text is not straightforward because the text may have less lexical overlap yet convey the same information. On top of that, non-novel/redundant information in a document may have assimilated from multiple source documents, not just one. The problem surmounts when the subject of the discourse is documents, and numerous prior documents need to be processed to ascertain the novelty/non-novelty of the current one in concern. In this work, we build upon our earlier investigations for document-level novelty detection and present a comprehensive account of our efforts toward the problem. We explore the role of pre-trained Textual Entailment (TE) models to deal with multiple source contexts and present the outcome of our current investigations. We argue that a multipremise entailment task is one close approximation toward identifying semantic-level non-novelty. Our recent approach either performs comparably or achieves significant improvement over the latest reported results on several datasets and across several related tasks (paraphrasing, plagiarism, rewrite). We critically analyze our performance with respect to the existing state of the art and show the superiority and promise of our approach for future investigations. We also present our enhanced dataset TAP-DLND 2.0 and several baselines to the community for further research on document-level novelty detection.

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de-Lima-Santos, Mathias-Felipe, and Wilson Ceron. "Artificial Intelligence in News Media: Current Perceptions and Future Outlook." Journalism and Media 3, no. 1 (December 30, 2021): 13–26. http://dx.doi.org/10.3390/journalmedia3010002.

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In recent years, news media has been greatly disrupted by the potential of technologically driven approaches in the creation, production, and distribution of news products and services. Artificial intelligence (AI) has emerged from the realm of science fiction and has become a very real tool that can aid society in addressing many issues, including the challenges faced by the news industry. The ubiquity of computing has become apparent and has demonstrated the different approaches that can be achieved using AI. We analyzed the news industry’s AI adoption based on the seven subfields of AI: (i) machine learning; (ii) computer vision (CV); (iii) speech recognition; (iv) natural language processing (NLP); (v) planning, scheduling, and optimization; (vi) expert systems; and (vii) robotics. Our findings suggest that three subfields are being developed more in the news media: machine learning, computer vision, and planning, scheduling, and optimization. Other areas have not been fully deployed in the journalistic field. Most AI news projects rely on funds from tech companies such as Google. This limits AI’s potential to a small number of players in the news industry. We made conclusions by providing examples of how these subfields are being developed in journalism and presented an agenda for future research.

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Nishida, Toyoaki. "Augmenting Conversational Environment." International Journal of Cognitive Informatics and Natural Intelligence 6, no. 4 (October 2012): 103–24. http://dx.doi.org/10.4018/jcini.2012100105.

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People are proficient in collaboratively forming and maintaining gatherings thereby shaping and cultivating collective thoughts through fluent conversational interactions. A big challenge is to develop a technology for augmenting the conversational environment so that people can conduct even better conversational interactions for collective intelligence and creation. Conversational informatics is a field of research that focuses on investigating conversational interactions and designing intelligent artifacts that can augment conversational interactions. The field draws on a foundation provided by artificial intelligence, natural language processing, speech and image processing, cognitive science, and conversation analysis. In this article, the author overviews a methodology for developing augmented conversational environment and major achievements. The author also discusses issues for making agents empathic so that they can induce sustained and constructive engagement with people.

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Beenish Zahra. "ARTIFICIAL INTELLIGENCE AND CYC." Lahore Garrison University Research Journal of Computer Science and Information Technology 1, no. 4 (December 29, 2017): 29–36. http://dx.doi.org/10.54692/lgurjcsit.2017.010412.

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Since 1984, it is enormous work going on for the accomplishing of the project Cyc (‗Saik‘). This work is based on knowledge of ―Artificial Intelligence‖ which is developed by the Cycorpcompany and by Douglas Lenat at MCC. It‘s a Microelectronics and Computer Technology Corporation (MCC) part for so long. The dominant aim of Cycorp to develop this system is to just clarify anything in semantical determination rather than syntactically determination of words commands by the machine in which Cyc is installed to do some job. The other objective was in the building of Cyc is to codify, in a form which is usable by the machine, where knowledge‘s millions piece that composes common sense of a normal human or the common sense made in the human brain. Cyc presents a proprietary schema of knowledge representation that utilized first-order relationships. The relationships that presents between first-order logic (FOL) and first-order theory (FOT). After a long time, in1986, Cyc’s developer (Douglas Lenat) estimate that the total effort required to complete Cyc project would be 250,000 rules and 350 man-years. In 1994, Cyc Project was the reason behind creating independency into Cycorp, in Austin, Texas. As it is a common phrase that "Every tree is a plant" and "Plants die eventually" so that why by the mean of this some knowledge representing pieces which are in the database are like trees and plants like structures. The engine is known as an inference engine, able to draw the obvious results and answer the questions correctly on asking that whether trees die. The Knowledge Base (KB) system, which is included in Cyc, contains more than one million humans like assertions, rules or commonsense ideas. These ideas, rules, and human-defined assertions are describing or formatted in the language known as CycL. They are based on the predication of calculus and many otherhuman-based sciences, which has syntax similar to that of the language ―LISP‖. Though some extend the work on the Cyc project continues as a ―Knowledge Engineering‖, which represents some facts about the world, and implementing effective mechanisms which are derived after reaching the basic level conclusion on that knowledge. As Cyc include the firstorder logic and first order theory, which exist in some relationship; so it definitely uses and handle some other branches for human-interaction like mathematics, philosophy, and linguistics. However, increasingly, the other aim of Cycorp while developing Cyc is involvingan ability, which is given to the Cyc system that it can communicate with end users, by use of CycL, processing of natural language, and can assist with the knowledge formation process through the machine learning.

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Journal articles on the topic 'Natural language processing (Computer science) Artificial intelligence'

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