Статті в журналах: "Intelligent language processing"

1

Zou, Xiao Hua. "The Study on the Framework of Intelligence Information Processing." Applied Mechanics and Materials 20-23 (January 2010): 861–66. http://dx.doi.org/10.4028/www.scientific.net/amm.20-23.861.

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It is proposed to get the fundamental theory of intelligent information processing with combining natural language understanding and computational intelligence. Considering applied fields on intelligent classification, indexing, retrieval, abstract and machine translation, natural language understanding can provide theoretical framework and computational intelligence will give technical realization.

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2

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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3

Nagata, Noriko. "An Effective Application of Natural Language Processing in Second Language Instruction." CALICO Journal 13, no. 1 (January 14, 2013): 47–67. http://dx.doi.org/10.1558/cj.v13i1.47-67.

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This paper presents an intelligent CALI system called "Nihongo-CALI" (Japanese Computer Assisted Language Instruction), which employs natural language processing to provide immediate, grammatically sophisticated feedback to students in an interactive environment. Using this system, Nagata (1993) previously compared the effectiveness of the two different levels of computer feedback for teaching Japanese passive sentences: traditional feedback (which follows simple pattern-matching error analysis and indicates only missing/unexpected words in the learners' responses) and intelligent feedback (which utilizes a parsing technique to provide detailed grammatical explanations for the source of the learners' errors). The study found a statistically significant difference between traditional and intelligent feedback, favoring intelligent feedback. The present study compares the efficacy of intelligent CALI feedback with that of a more advanced, traditional CALI feedback (which also indicates the positions of missing particles in the learners' responses) for teaching basic sentence constructions in Japanese. The result indicates that the Intelligent CALI feedback is more effective than even the enhanced version of traditional CALI feedback, underscoring the importance of natural language processing technology in second language instruction.

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4

Pramada, Sawant. "Intelligent Sign Language Recognition Using Image Processing." IOSR Journal of Engineering 03, no. 02 (February 2013): 45–51. http://dx.doi.org/10.9790/3021-03224551.

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5

Pinto, David, Beatriz Beltrán, and Vivek Singh. "Recent advances in language & knowledge engineering." Journal of Intelligent & Fuzzy Systems 42, no. 5 (March 31, 2022): 4299–305. http://dx.doi.org/10.3233/jifs-219220.

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Language & Knowledge Engineering is essential for the successfully development of artificial intelligence. The technologies proposed in international forums are meant to improve all areas of our daily life whether it is related to production industries, social communities, government, education, or something else. We consider very important to reveal the recent advances Intelligent and Fuzzy Systems applied to Language & Knowledge Engineering because they are the base for the society of tomorrow. Thus, the aim of this special issue of Journal of Intelligent and Fuzzy Systems is to present a collection of papers that cover recent research results on the two wide topics: language and knowledge engineering. Even if the special issue is structured into these two general topics, we have covered specific themes such as the following ones: Natural Language Processing, Knowledge engineering, Pattern recognition, Artificial Intelligence and Language, Information Processing, Machine Learning Applied to Text Processing, Image and Text Classification, Multimodal data analysis, sentiment analysis, etc.

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6

Chen, Keliang, Yunxiao Zu, and Weizheng Ren. "Research and Design of Knowledge System Construction System Based on Natural Language Processing." International Journal of Pattern Recognition and Artificial Intelligence 33, no. 12 (November 2019): 1959038. http://dx.doi.org/10.1142/s0218001419590389.

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The digital processing of content resources has subverted the traditional paper content processing model and has also spread widely. The digital resources processed by text structure need to be structured and processed by professional knowledge, which can be saved as a professional digital content resource of knowledge base and provide basic metadata for intelligent knowledge service platform. The professional domain-based knowledge system construction system platform explored in this study is designed based on natural language processing. Natural language processing is an important branch of artificial intelligence, which is the application of artificial intelligence technology in linguistics. The system first extracts the professional thesaurus and domain ontology in the digital resources and then uses the new word discovery algorithm based on the label weight designed by artificial intelligence technology to intelligently extract and clean the new words of the basic thesaurus. At the same time, the relationship system between knowledge points and elements is established to realize the association extraction of targeted knowledge points, and finally the output content is enriched from knowledge points into related knowledge systems. In order to improve the scalability and universality of the system, the extended architecture of the thesaurus, algorithms, computational capabilities, tags, and exception thesaurus was taken into account when designing. At the same time, the implementation of “artificial intelligence [Formula: see text] manual assistance” was adopted. On the basis of improving the system availability, the experimental basis of the optimization algorithm is provided. The results of this research will bring an artificial intelligence innovation after the digitization to the publishing industry and will transform the content service into an intelligent service based on the knowledge system.

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7

Shah, Rishabh, Siddhant Lahoti, and K. Lavanya. "An intelligent chat-bot using natural language processing." International Journal of Engineering Research 6, no. 5 (2017): 281. http://dx.doi.org/10.5958/2319-6890.2017.00019.8.

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8

Wei, Tan Chia, Mohd Hanafi Ahmad Hijazi, Suraya Alias, Ag Asri Ag Ibrahim, and Mohd Fairuz Iskandar Othman. "Intelligent Course Recommender Chatbot Using Natural Language Processing." International Journal on Advanced Science, Engineering and Information Technology 12, no. 5 (September 20, 2022): 1915. http://dx.doi.org/10.18517/ijaseit.12.5.14798.

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9

McSHANE, MARJORIE, and SERGEI NIRENBURG. "A KNOWLEDGE REPRESENTATION LANGUAGE FOR NATURAL LANGUAGE PROCESSING, SIMULATION AND REASONING." International Journal of Semantic Computing 06, no. 01 (March 2012): 3–23. http://dx.doi.org/10.1142/s1793351x12400016.

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OntoAgent is an environment that supports the cognitive modeling of societies of intelligent agents that emulate human beings. Like traditional intelligent agents, OntoAgent agents execute the core functionalities of perception, reasoning and action. Unlike most traditional agents, they engage in extensive "translation" functions in order to render perceived inputs into the unambiguous, ontologically-grounded knowledge representation language (KRL) that is used to model their knowledge, memory and reasoning. This paper describes the KRL of OntoAgent with a special focus on the many runtime functions used to translate between perceived inputs and the KRL, as well as to manipulate KRL structures for reasoning and simulation.

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10

Pikhart, Marcel. "Intelligent information processing for language education: The use of artificial intelligence in language learning apps." Procedia Computer Science 176 (2020): 1412–19. http://dx.doi.org/10.1016/j.procs.2020.09.151.

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11

Wang, Wei. "Machine Learning-Based Intelligent Scoring of College English Teaching in the Field of Natural Language Processing." Computational Intelligence and Neuroscience 2022 (August 4, 2022): 1–9. http://dx.doi.org/10.1155/2022/2754626.

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The current education evaluation is limited not only to the mode of simplification, indexing, and datafication, but also to the scientific nature of college teaching evaluation. This work firstly conducts a theoretical analysis of natural language processing technology, analyzes the related technologies of intelligent scoring, designs a systematic process for intelligent scoring of college English teaching, and finally conducts theoretical research on the Naive Bayesian algorithm in machine learning. In addition, the error of intelligent scoring of English teaching in colleges and universities and the accuracy of scoring and classification are analyzed and researched. The results show that the error between manual scoring and machine scoring is basically about 2 points and the minimum error of intelligent scoring in college English teaching under machine scoring can reach 0 points. There is a certain bias in manual scoring, and scoring on the machine can reduce the generation of this error. The Naive Bayes algorithm has the highest classification accuracy on the college intelligent scoring dataset, which is 76.43%. The weighted Naive Bayes algorithm has been improved in the classification accuracy of college English teaching intelligent scoring, with an average accuracy rate of 74.87%. To sum up, the weighted Naive Bayes algorithm has better performance in the classification accuracy of college English intelligent scoring. This work has a significant effect on the scoring of the college intelligent teaching scoring system under natural language processing and the classification of college teaching intelligence scoring under the Naive Bayes algorithm, which can improve the efficiency of college teaching scoring.

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12

Chen, Yi-Hui, Eric Jui-Lin Lu, and Ting-An Ou. "Intelligent SPARQL Query Generation for Natural Language Processing Systems." IEEE Access 9 (2021): 158638–50. http://dx.doi.org/10.1109/access.2021.3130667.

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13

Morris, D. T., and D. E. Asumu. "Intelligent natural language interface for a signal processing system." IEE Proceedings E Computers and Digital Techniques 137, no. 5 (1990): 371. http://dx.doi.org/10.1049/ip-e.1990.0046.

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14

Zhang, Zeliang, and Xinwen Bi. "Research and Experiment of Intelligent Natural Language Processing Algorithms." Wireless Personal Communications 102, no. 4 (January 19, 2018): 2927–39. http://dx.doi.org/10.1007/s11277-018-5316-2.

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15

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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16

Amaral, Luiz, Detmar Meurers, and Ramon Ziai. "Analyzing learner language: towards a flexible natural language processing architecture for intelligent language tutors." Computer Assisted Language Learning 24, no. 1 (January 25, 2011): 1–16. http://dx.doi.org/10.1080/09588221.2010.520674.

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17

Ekpenyong, Moses Effiong, Eno-Abasi Essien Urua, Aniefon Daniel Akpan, Olufemi Sunday Adeoye, and Aminu Alhaji Suleiman. "A Template-Based Approach to Intelligent Multilingual Corpora Transcription." International Journal of Humanities and Arts Computing 16, no. 2 (October 2022): 182–213. http://dx.doi.org/10.3366/ijhac.2022.0290.

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Emerging linguistic problems are data-driven and multidisciplinary, requiring richly transcribed corpora. Accurate corpus transcription therefore demands intelligent protocols that satisfy the following important criteria: 1) acceptability by end-users, computers/machines; 2) conformity to existing language standards, rules and structures; and 3) representation within the context of the intended language domain. To demonstrate the feasibility of these criteria, a template-based framework for multilingual transcription was proposed and implemented. The first version of the developed transcription tool, also called SCAnnAL (Speech Corpus Annotator for African Languages), applies signal processing to pre-segment waveforms of a recorded speech corpus, into word, syllable and phoneme units, resulting in a pre-segmented TextGrid file with empty labels. Using preformatted templates, the front-end or linguistic aspects/datasets (the text corpus, vowels inventory, consonants inventory, and a set of syllabification rules) are specified in a default language. A Natural Language Understanding (NLU) algorithm then uses these datasets with a data-driven syllabification algorithm to relabel subtrees of the TextGrid file. Tone pattern models were finally constructed from translations of experimental data, using the Ibadan 400 words (a list of basic items of a language), for four Nigerian tone languages. Integration of the tone pattern models into the transcription system is expected in a future paper. This research will benefit emerging digital humanists and computational linguists working on language data, as well as open new opportunities for improved African tone language speech processing systems.

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18

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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19

Gan, Qiying. "Research on the emotional cognitive evaluation model based on artificial neural network." E3S Web of Conferences 145 (2020): 01040. http://dx.doi.org/10.1051/e3sconf/202014501040.

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the neural network, fuzzy set theory and evolutionary algorithm in artificial intelligence are all intelligent information processing theories that follow the biological processing mode. These theories are realized by rational logical thinking mode without considering the role of human perceptual thinking in the information processing process, such as emotion and cognition. Among them, the neural network mainly imitates the function of the mental system of human, adopts the method from the bottom to the top, and processes the difficult language pattern information through a large number of complicated connections of neurons. Artificial neural network (Ann) is a cross research field of artificial intelligence and life science. This theory mainly imitates the information processing mechanism of organisms in nature and is mainly used in intelligent information processing systems that can adapt to long-term changes in the environment. Therefore, neural network has important application significance in the research of intelligence, robot and artificial emotion.

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20

Park, Jong-Hyun, and Ji-Hoon Kang. "Intelligent service processing in common USN middleware." Artificial Intelligence Review 35, no. 1 (November 3, 2010): 37–51. http://dx.doi.org/10.1007/s10462-010-9182-x.

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21

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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22

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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McNamara, Danielle S., Scott A. Crossley, and Rod Roscoe. "Natural language processing in an intelligent writing strategy tutoring system." Behavior Research Methods 45, no. 2 (October 6, 2012): 499–515. http://dx.doi.org/10.3758/s13428-012-0258-1.

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24

Caldarini, Guendalina, Sardar Jaf, and Kenneth McGarry. "A Literature Survey of Recent Advances in Chatbots." Information 13, no. 1 (January 15, 2022): 41. http://dx.doi.org/10.3390/info13010041.

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Chatbots are intelligent conversational computer systems designed to mimic human conversation to enable automated online guidance and support. The increased benefits of chatbots led to their wide adoption by many industries in order to provide virtual assistance to customers. Chatbots utilise methods and algorithms from two Artificial Intelligence domains: Natural Language Processing and Machine Learning. However, there are many challenges and limitations in their application. In this survey we review recent advances on chatbots, where Artificial Intelligence and Natural Language processing are used. We highlight the main challenges and limitations of current work and make recommendations for future research investigation.

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25

Ansari, Tarique. "Conversational AI Assistant." International Journal for Research in Applied Science and Engineering Technology 10, no. 11 (November 30, 2022): 1169–72. http://dx.doi.org/10.22214/ijraset.2022.47554.

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Abstract: A conversational assistant is an intelligent conversational computing system designed to mimic human speech to provide automated online guidance and support. The growing benefits of conversational support have led to widespread adoption across many industries to provide virtual support to their customers. Conversation assistance uses methods and algorithms from his two fields of artificial intelligence: natural language processing and machine learning. However, the application has many challenges and limitations. This research reviews recent advances in conversation support using artificial intelligence and natural language processing. We highlight the main challenges and limitations of the current work and provide recommendations for future research investigations.

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Attrey, Rohin, and Alexander Levit. "The promise of natural language processing in healthcare." University of Western Ontario Medical Journal 87, no. 2 (March 12, 2019): 21–23. http://dx.doi.org/10.5206/uwomj.v87i2.1152.

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The healthcare industry generates data at a rapid rate, with no signs of slowing down. A large portion of this information takes the form of unstructured narrative text, making it difficult for computer systems to analyze the data in a usable format. However, automated analysis of this information could be incredibly useful in daily practice. This could be accomplished with natural language processing, an area of artificial intelligence and computational linguistics that is used to analyze and process large sets of unstructured data, namely spoken or written communication. Natural language processing has already been implemented in many sectors, and the industry is projected to be worth US$16 billion by 2021. Natural language processing could take unstructured patient data and interpret meaning from the text, allowing that information to inform healthcare delivery. Natural language processing can also enable intelligent chatbots, interacting and providing medical support to patients. It has the potential to aid physicians by efficiently summarizing patient charts and predicting patient outcomes. In hospitals, it has the ability to analyze patient satisfaction and facilitate quality improvement. Despite current technical limitations, natural language processing is a rapidly developing technology that promises to improve the quality and efficiency of healthcare delivery.

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27

Longwei, Q. "Ontological approach to Chinese text processing." Doklady BGUIR 18, no. 6 (October 1, 2020): 49–56. http://dx.doi.org/10.35596/1729-7648-2020-18-6-49-56.

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To implement natural language user interface and an intelligent answer to questions, the knowledgebased semantic model for Chinese language processing is proposed. The article gives careful consideration to the existing methods and various knowledge bases for natural language processing. The analysis of these methods has led to the conclusion that in natural language processing, the knowledge base is the most fundamental and crucial part. The knowledge base makes it possible to ensure processing of a natural language based on initially described knowledge and to explain the processing operations. By virtue of the analysis of various methods for constructing knowledge bases about the English and Chinese languages, an ontological approach to the Chinese language processing was proposed. The Chinese language processing model has two main aspects: the design of knowledge base about the Chinese language and the development of ontology-based knowledge processing machine. The proposed approach is aimed at developing a semantic model of knowledge on the Chinese language. As a stage in the implementation of the approach, I designed the ontology of the Chinese language that can be applied for further processing of the language. This paper considers the preliminary version of the ontology and the principle of building a knowledge base about the Chinese language. There are no uniform standards and evaluation system for designing an ontology. Expansion, refinement and evaluation of the ontology require further research.

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28

Nagata, Noriko. "Computer vs. Workbook Instruction in Second Language Acquisition." CALICO Journal 14, no. 1 (January 14, 2013): 53–75. http://dx.doi.org/10.1558/cj.v14i1.53-75.

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Many studies have failed to demonstrate the effectiveness of CALI (Computer Assisted Language Instruction) as compared to non-CALI instruction, and the pedagogical significance of CALI programs has been questioned. This study employs an intelligent CALI program, called Nihongo-CALI (Japanese Computer Assisted Language Instruction), which uses natural language processing to provide more sophisticated error analysis and feedback than conventional CALI programs. The study compares the effectiveness of Nihongo-CALI with non-CALI workbook instruction, in the form of self-study lessons. The results of the study show that given the same grammar notes and exercises, ongoing intelligent computer feedback is more effective than simple workbook answer sheets for developing learners' grammatical skill in producing Japanese particles and sentences. The results emphasize the importance of providing an intelligent level of feedback to the learner, a task for which natural language processing is well suited.

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Zhu, Min. "The Application of Intelligent Speech Analysis Technology in the Spoken English Language Learning Model." Mobile Information Systems 2022 (May 9, 2022): 1–11. http://dx.doi.org/10.1155/2022/3192892.

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In order to improve the effect of spoken English processing, it is necessary to improve the spoken English processing technology from the perspective of the characteristics of spoken English, combined with intelligent algorithms. This paper combines the intelligent speech analysis technology to improve the spoken English recognition technology and combines the actual and needs of English learning to improve the system algorithm. Moreover, this paper combines the intelligent speech analysis to construct the intelligent spoken English learning model structure and combines the statistical method and the intelligent evaluation method to analyze the model effect. After obtaining the system function structure, this paper designs experiments to verify the effect of the model proposed in this paper. From the experimental analysis results, it can be seen that the intelligent English speech analysis model proposed in this paper can play an important role in the learning of spoken English.

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Adeel Munawar. "Domain-Independent Natural Language Processing of text using Unsupervised Translation." Lahore Garrison University Research Journal of Computer Science and Information Technology 4, no. 3 (September 30, 2020): 71–80. http://dx.doi.org/10.54692/lgurjcsit.2020.0403102.

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NLP is one of the very important domains of artificial intelligence. Nowadays, advancements are being made and NLP is one of the most developing fields. In this paper, we offer a mutual use of unsupervised translation with n-grams and Natural Language Processing techniques to challenge the difficulty of unsupervised translation extraction from textual data. To build a Text Meaning Extraction System, we have to deliver one important element which is input text. This studypresented a different algorithm to work out resemblances between natural languages, by using sequence package analysis and changing them into n-grams. Whenever the sentences that are grammatically difficult and quite lengthy are applied to see the results of the presented algorithm, there are quite efficient results in a semantic reaction. To enhance the experience in the field of AI and search engines, this research paper shows how to improve the handling capability of fuzzy concepts within computers. For example, when search jobs are executed in search engines small textual concepts or sentences might be semantically formed to switch the keyword-based queries. This ability may be functional to intelligent agents to even the procedure of communication between humans and machinery.

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Kang, Haiyan, and Xiaojiao Yuan. "Natural Language Processing Technologies for Multi-Level Intelligent Spam Mail-Filter." International Journal of Machine Learning and Computing 4, no. 3 (June 2014): 271–74. http://dx.doi.org/10.7763/ijmlc.2014.v4.423.

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32

Herath, S., S. Ishizaki, T. Ikeda, Y. Anzai, and H. Aiso. "MACHINE PROCESSING OF SINHALA NATURAL LANGUAGE: A STEP TOWARD INTELLIGENT SYSTEMS." Cybernetics and Systems 22, no. 3 (May 1991): 331–48. http://dx.doi.org/10.1080/01969729108902286.

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33

Kacalak, Wojciech, Maciej Majewski, Keith Douglas Stuart, and Zbigniew Budniak. "Interactive Systems for Designing Machine Elements and Assemblies." Management and Production Engineering Review 6, no. 3 (September 1, 2015): 21–34. http://dx.doi.org/10.1515/mper-2015-0023.

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Abstract The article describes the development of fundamentals of machine elements and assemblies design processes automation using artificial intelligence, and descriptions of structural elements’ features in a natural language. In the proposed interactive automated design systems, computational artificial intelligence methods allow communication by speech and natural language, resulting in analyses of design engineer’s messages, analyses of constructions, encoding and assessments of constructions, CAD system controlling and visualizations. The system is equipped with several adaptive intelligent layers for human biometric identification, recognition of speech and handwriting, recognition of words, analyses and recognition of messages, enabling interpretation of messages, and assessments of human reactions. The article proposes a concept of intelligent processing for analysis of descriptions of machine elements’ structural features in a natural language. It also presents the developed methodology for similarity analysis between structural features of designed machine elements and corresponding antipatterns allowing normalization of parameters of the analysed structural solutions.

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Yan, Xin, and Junhui Yan. "Design and Implementation of Interactive Platform for Operation and Maintenance of Multimedia Information System Based on Artificial Intelligence and Big Data." Computational Intelligence and Neuroscience 2022 (May 4, 2022): 1–9. http://dx.doi.org/10.1155/2022/4620930.

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In order to cope with the challenges that operators face after the impact of diversified social channels in the field of interactive services, we actively build a new generation of intelligent interactive systems based on artificial intelligence technology, a semantic understanding, and intention recognition, covering rich media content, omnichannel coverage, high-frequency knowledge updates, consistent service response, high-quality, and low-cost intelligent interactive solutions is proposed. The solution provides overall business modeling for scenario design and a scenario-based knowledge expression system, with the function of fragmented knowledge processing. With complete text and voice information, combined with pictures, text, audio, video, and other multimedia, we intelligently interact with users, allowing users to obtain required information and solve problems in a pleasant and relaxing interaction. Therefore, the research and exploration of the intelligent interactive system architecture based on artificial intelligence is a useful practice and strong support for operators to redefine the connotation and elements of “smart service” in the process of building “smart operation.” Through repeated tests, it can be seen that the language similarity has reached 0.75549, which is very close to 1.0000. It can be seen that the design of this platform has been successful.

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35

Warschauer, Mark. "INTELLIGENT LANGUAGE TUTORS: THEORY SHAPING TECHNOLOGY.V. Melissa Holland, Jonathan D. Kaplan, & Michelle R. Sams (Eds.). Mahwah, NJ: Erlbaum, 1995. Pp. xviii + 384. $79.95 cloth, $39.95 paper." Studies in Second Language Acquisition 20, no. 3 (September 1998): 445–46. http://dx.doi.org/10.1017/s0272263198273074.

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This book presents and discusses efforts to develop Intelligent Computer Assisted Language Learning (ICALL) programs based on advances in artificial intelligence (AI) and natural language processing (NLP). Sixteen of the book's 20 chapters provide descriptions of particular ICALL programs, divided into three categories: text-based language tutors and learning environments, dialogue-based language games, and graphics-based language tutors and learning environments. Four chapters at the end offer general commentary on ICALL from the perspectives of experimental psychology (by Brian MacWhinney), linguistics and AI (by Alan Bailin), second language acquisition theory (by Nina Garret), and educational theory (by Rebecca Oxford).

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Chen, Lin. "Power Intelligent Customer Service Robot Based on Artificial Intelligence." Journal of Physics: Conference Series 2066, no. 1 (November 1, 2021): 012048. http://dx.doi.org/10.1088/1742-6596/2066/1/012048.

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Abstract With the rapid development of speech recognition technology, voice chat robot has become a breakthrough of artificial intelligence. Voice chat robot should be a typical application field of customer service, providing customers with efficient and convenient service all day. The traditional customer service center is mainly based on telephone service, facing the problems of large number of customers, high maintenance cost, slow knowledge update, limited service time, low training cost and so on. So, at the same time, the use habits of customers have also changed fundamentally. The vast majority of services and transactions can be carried out through the Internet, such as Taobao and Jingdong. However, the quality and cost of voice services can be greatly reduced through the interaction between robots and channelization voice service centers. Through the research and development of natural language technology, an intelligent and centralized mobile communication service application platform is constructed by using we-chat platform. Through natural language processing, machine learning, big data computing and other technological innovation, we focus on the use of online robot recognition to understand customer problems and timely feedback customer needs. The results show that in the statistics of customer service machine problems, the highest proportion of consumers’ problems about payment is 37%, and the lowest is 29%.

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Bryndin, Evgeny. "Functional and Harmonious Selforganization of Large Intellectual Agent Ensembles with Smart Hybrid Competencies via Wireless and Mobile Networks." International Journal of Wireless & Mobile Networks 13, no. 05 (October 31, 2021): 1–13. http://dx.doi.org/10.5121/ijwmn.2021.13501.

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Artificial intelligence of large ensembles of intelligent agents in terms of computing power surpasses human intelligence. He is capable of artificial thinking and understanding. Giant ensembles of intellectual agents with artificial consciousness and intelligence are able, for the results set by the person necessary for him, to find solutions for their obtaining on the basis of professional competence and experience accumulation. The professional competence of artificial intelligence is the ability to use technologies, including computer vision, natural language processing, speech recognition and synthesis, intelligent decision support, as well as the use of synergistic methods, functional self-organizing methods and utility and preference criteria. For artificial intelligence, the functional organization of the system is important. The functionalism of artificial intelligence does not depend on its carrier, allows many methods of its functional implementation, as well as to form the completeness of its functions. Giant ensembles of intellectual agents with a full set of functions gradually and flexibly form events into solutions or rational behavior to obtain a given necessary result. Intelligent artificial intelligence has psychological, analytical, research, language, professional and behavioral hybrid competencies. Each competence is exercised by an intelligent agent with a competent functional professional manner. Intelligent agents form an ensemble with intelligent ethical artificial intelligence. The article is devoted to functional harmonious selforganization of ensembles of intellectual agents.Functional harmonious self-organization of the interaction of intellectual agents in different environments is carried out via wireless and mobile networks on the basis of data of a specific environment obtained by analytical competent intellectual agents. As result of research, the law of the golden section of the functional harmonious self-organization of ensembles of intellectual agents was revealed. Further research will focus on the effective use of wireless and mobile networks in the practical application of smart agent ensembles.

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38

Chao, Min-Hua, Amy J. C. Trappey, and Chun-Ting Wu. "Emerging Technologies of Natural Language-Enabled Chatbots: A Review and Trend Forecast Using Intelligent Ontology Extraction and Patent Analytics." Complexity 2021 (May 24, 2021): 1–26. http://dx.doi.org/10.1155/2021/5511866.

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Natural language processing (NLP) is a critical part of the digital transformation. NLP enables user-friendly interactions between machine and human by making computers understand human languages. Intelligent chatbot is an essential application of NLP to allow understanding of users’ utterance and responding in understandable sentences for specific applications simulating human-to-human conversations and interactions for problem solving or Q&As. This research studies emerging technologies for NLP-enabled intelligent chatbot development using a systematic patent analytic approach. Some intelligent text-mining techniques are applied, including document term frequency analysis for key terminology extractions, clustering method for identifying the subdomains, and Latent Dirichlet Allocation for finding the key topics of patent set. This research utilizes the Derwent Innovation database as the main source for global intelligent chatbot patent retrievals.

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39

Rus, Vasile, Sidney D’Mello, Xiangen Hu, and Arthur Graesser. "Recent Advances in Conversational Intelligent Tutoring Systems." AI Magazine 34, no. 3 (September 15, 2013): 42–54. http://dx.doi.org/10.1609/aimag.v34i3.2485.

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We report recent advances in intelligent tutoring systems with conversational dialogue. We highlight progress in terms of macro and microadaptivity. Macroadaptivity refers to a system’s capability to select appropriate instructional tasks for the learner to work on. Microadaptivity refers to a system’s capability to adapt its scaffolding while the learner is working on a particular task. The advances in macro and microadaptivity that are presented here were made possible by the use of learning progressions, deeper dialogue and natural language processing techniques, and by the use of affect-enabled components. Learning progressions and deeper dialogue and natural language processing techniques are key features of DeepTutor, the first intelligent tutoring system based on learning progressions. These improvements extend the bandwidth of possibilities for tailoring instruction to each individual student which is needed for maximizing engagement and ultimately learning.

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40

Ferro, M. Vilares, M. Fernández Gavilanes, A. Blanco González, and C. Gómez-Rodríguez. "Intelligent Retrieval for Biodiversity." International Journal on Artificial Intelligence Tools 25, no. 01 (February 2016): 1550029. http://dx.doi.org/10.1142/s0218213015500293.

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A proposal for intelligent retrieval in the biodiversity domain is described. It applies natural language processing to integrate linguistic and domain knowledge in a mathematical model for information management, formalizing the notion of semantic similarity in different degrees. The goal is to provide computational tools to identify, extract and relate not only data but also scientific notions, even if the information available to start the process is not complete. The use of conceptual graphs as a basis for interpretation makes it possible to avoid the use of classic ontologies, whose start-up requires costly generation and maintenance protocols and also unnecessarily overload the accessing task for inexpert users. We exploit the automatic generation of these structures from raw texts through graphical and natural language interaction, at the same time providing a solid logical and linguistic foundation to sustain the curation of databases.

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Rokkam, Krishna Vamsi. "An Intelligent TLDR Software for Summarization." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (June 30, 2022): 2852–55. http://dx.doi.org/10.22214/ijraset.2022.44508.

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Abstract: The amount of textual data available from diverse resources is increasing dramatically in the substantial data age. This textual volume has a wealth of information and expertise that must be skilfully summarised in order to be useful. Because billions of articles are published every day, it takes a long time to look through and keep up with all of the information available. Much of this text material has to be reduced to shorter, focused summaries that capture the most important aspects, both so we can explore it more efficiently and to ensure that the bigger papers include the information we need. Because manual text summarising is a time-consuming and typically difficult activity, automating it is expanding in popularity and thus provides an ideal impetus for academic study. The growing availability of documents has necessitated much study in the field of natural language processing (NLP) for automatic text summarization. "Is there any software that can assist us digest the facts more efficiently and in less time?" is the genuine question. As a result, the major goal of the summarization system is to extract the most important information from the data and deliver it to the consumers. In NLP, summarization is the act of condensing text information in huge texts to make it easier to understand and consume. We suggest a solution by developing a text summary programme that uses Natural Language Processing and accepts an input (plain text or text scrapped from a website). The output is the outlined text. Natural language processing, along with machine learning, makes it easier to condense large quantities of information into a coherent and fluent summary that only incorporates the article's most important points.

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42

N, Sreevidya. "PERSONALIZED INTELLIGENT VOICE ASSISTANT ANNA FOR SPEECH RECOGNITION USING NATURAL LANGUAGE PROCESSING." international journal of advanced research in computer science 13, no. 3 (June 21, 2022): 26–29. http://dx.doi.org/10.26483/ijarcs.v13i3.6833.

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43

Alfawareh, Hejab, and Shaidah Jusoh. "Intelligent decision support system for CV evaluation based on natural language processing." International Journal of ADVANCED AND APPLIED SCIENCES 6, no. 4 (April 2019): 1–8. http://dx.doi.org/10.21833/ijaas.2019.04.001.

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44

Marzouk, Radwa, Eatedal Alabdulkreem, Mohamed K. Nour, Mesfer Al Duhayyim, Mahmoud Othman, Abu Sarwar Zamani, Ishfaq Yaseen, and Abdelwahed Motwakel. "Natural Language Processing with Optimal Deep Learning-Enabled Intelligent Image Captioning System." Computers, Materials & Continua 74, no. 2 (2023): 4435–51. http://dx.doi.org/10.32604/cmc.2023.033091.

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45

Prakash, Varuna, Dominique Piquette, and Andre Carlos Kajdacsy-Balla Amaral. "The Artificially Intelligent Teacher: Applying Natural Language Processing to Critical Care Education." ATS Scholar 3, no. 4 (December 2022): 505–8. http://dx.doi.org/10.34197/ats-scholar.2022-0114ed.

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46

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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47

Burston, Jack. "Towards Better Tutorial CALL: A Matter of Intelligent Control." CALICO Journal 6, no. 4 (January 14, 2013): 75–89. http://dx.doi.org/10.1558/cj.v6i4.75-89.

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Although drill and practice exercises continue to account for a substantial proportion of CALL production, most available foreign language programs suffer from expedient programming and poor pedagogy. At the heart of the problem is the failure of language teaching specialists to involve themselves in CALL research and development. In the absence of AI-derived natural language processors, programs based on anticipated student responses remain the only viable means of creating sophisticated CALL lessons. Special purpose authoring languages offer the most accessible approach to such programming, but are seriously hampered by their lack of flexible feedback capabilities. In particular, much greater author control is needed over pattern matching devices to accommodate the processing of non-differential variations in predicted answers. Provided that basic command structures incorporate variable parameters with pre-set, but alterable, default values, increased author management of response handling need not place unreasonable programming denmands upon the courseware designer. In any event, variable lesson parameters are required to enable students to more intelligently exploit the computer-aided learning environment. To demonstrate the feasibility of implementing flexible response handling control, the essential design features of an Australian developed authoring language, EMU, are examined in some detail.

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48

Kobayashi, Ichiro, Michio Sugeno, Toru Sugimoto, Shino Iwashita, Noriko Ito, Michiaki Iwazume, and Yusuke Takahashi. "Everyday-Language Computing Project Overview." Journal of Advanced Computational Intelligence and Intelligent Informatics 10, no. 6 (November 20, 2006): 773–81. http://dx.doi.org/10.20965/jaciii.2006.p0773.

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This paper explains an overview of everyday language computing (ELC) promoted by the Laboratory for Language-Based Intelligent Systems, Brain Science Institute, RIKEN from 2000 to 2005. The objective of ELC was to develop a language-based intelligent system. To do this, we constructed a computational model of language in context, called the Semiotic Base, and developed computational algorithms for text understanding and generation, which are basic information processing in the ELC framework. Based on these resources and algorithms, we constructed a computing environment in which language is used as an information medium to process information. To demonstrate its feasibility, we developed language-based applications such as a language-based wordprocessor, language-based programming, and “smart” help. We explain the basic principles of ELC, outline of its basic technologies, and discuss applications developed based on the ELC framework.

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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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Amaral, Luiz A., and Detmar Meurers. "On using intelligent computer-assisted language learning in real-life foreign language teaching and learning." ReCALL 23, no. 1 (January 5, 2011): 4–24. http://dx.doi.org/10.1017/s0958344010000261.

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AbstractThis paper explores the motivation and prerequisites for successful integration of Intelligent Computer-Assisted Language Learning (ICALL) tools into current foreign language teaching and learning (FLTL) practice. We focus on two aspects, which we argue to be important for effective ICALL system development and use: (i) the relationship between activity design and restrictions needed to make natural language processing tractable and reliable, and (ii) pedagogical considerations and the influence of activity design choices on the integration of ICALL systems into FLTL practice.

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