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Статті в журналах з теми "Stage machinery – dictionaries"
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Havrashenko, Anton, and Olesia Barkovska. "ANALYSIS OF TEXT AUGMENTATION ALGORITHMS IN ARTIFICIAL LANGUAGE MACHINE TRANSLATION SYSTEMS." Advanced Information Systems 7, no. 1 (March 13, 2023): 47–53. http://dx.doi.org/10.20998/2522-9052.2023.1.08.
Повний текст джерелаАнотація:
The work is devoted to the development of an organizational model of the machine translation system of artificial languages. The main goal is the analysis of text augmentation algorithms, which are significant elements of the developed machine translation system at the stage of improvement of new dictionaries created on the basis of already existing dictionaries. In the course of the work was developed a model of the machine translation system, created dictionaries based on texts and based on already existing dictionaries using augmentation methods such as back translation and crossover; improved dictionary based on algorithms of n-grams, Knuth-Morris-Pratt and word search in the text (such as binary search, tree search, sqrt decomposition). In addition, the work implements the possibility of using the prepared dictionary for translation. Obtained results can improve existing systems of machine translation of the text of artificial languages. Practical significance of this work is the analysis and improvement of text augmentation algorithms by changing the prefix tree type. Compared to the conventional algorithm, the improved algorithm reduced the memory usage by almost 13 times, which allows it to be used on much larger test data. This was achieved by changing the internal system of the node of the prefix tree from constant references to an expandable list.
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Laffling, John. "On Constructing a Transfer Dictionary for Man and Machine." Target. International Journal of Translation Studies 4, no. 1 (January 1, 1992): 17–31. http://dx.doi.org/10.1075/target.4.1.03laf.
Повний текст джерелаАнотація:
Abstract This article argues that, unlike conventional bilingual knowledge sources that are underpinned by the principle of filtering out a limited number of representative equivalents, transfer dictionaries that are derived from computer-assisted parallel text analysis can bring both the human and the mechanized translator a stage nearer the elusive ideal of text-insertable translation equivalents.
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Barkovska, Olesia, and Anton Havrashenko. "ANALYSIS OF WORD SEARCH ALGORITHMS IN THE DICTIONARIES OF MACHINE TRANSLATION SYSTEMS FOR ARTIFICIAL LANGUAGES." Computer systems and information technologies, no. 2 (June 29, 2023): 17–24. http://dx.doi.org/10.31891/csit-2023-2-2.
Повний текст джерелаАнотація:
The paper is devoted to the development of an organizational model of the machine translation system of artificial languages. The main goal is the analysis of word search algorithms in the dictionary, which are significant elements of the developed machine translation system at the stage of improvement of new dictionaries created on the basis of already existing dictionaries. In the course of the work was developed a model of the machine translation system, created dictionaries based on texts and based on already existing dictionaries using augmentation methods such as back translation and crossover; improved dictionary based on algorithms of n-grams, Knuth-Morris-Pratt and word search in the text (such as binary search, tree search, root decomposition search). In addition, the work implements the possibility of using the prepared dictionary for translation. The obtained results can improve existing systems of machine translation of the text of artificial languages. Namely, to reduce the operating time by approximately 20 times when switching from the balanced tree algorithm to other logarithmic algorithms. The practical significance of this work is the analysis and improvement of text augmentation algorithms using algorithms of binary search, hashes, search tree, and root decomposition.
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Pilikos, Georgios. "The relevance vector machine for seismic Bayesian compressive sensing." GEOPHYSICS 85, no. 4 (June 24, 2020): WA279—WA292. http://dx.doi.org/10.1190/geo2019-0200.1.
Повний текст джерелаАнотація:
Missing traces in seismic surveys create gaps in the data and cause problems in later stages of the seismic processing workflow through aliasing or incoherent noise. Compressive sensing (CS) is a framework that encompasses data reconstruction algorithms and acquisition processes. However, CS algorithms are mainly ad hoc by focusing on data reconstruction without any uncertainty quantification or feature learning. To avoid ad hoc algorithms, a probabilistic data-driven model is used, the relevance vector machine (RVM), to reconstruct seismic data and simultaneously quantify uncertainty. Modeling of sparsity is achieved using dictionaries of basis functions, and the model remains flexible by adding or removing them iteratively. Random irregular sampling with time-slice processing is used to reconstruct data without aliasing. Experiments on synthetic and field data sets illustrate its effectiveness with state-of-the-art reconstruction accuracy. In addition, a hybrid approach is used in which the domain of operation is smaller while, simultaneously, learned dictionaries of basis functions from seismic data are used. Furthermore, the uncertainty in predictions is quantified using the predictive variance of the RVM, obtaining high uncertainty when the reconstruction accuracy is low and vice versa. This could be used for the evaluation of source/receiver configurations guiding seismic survey design.
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Shyamasundar, L. B., and P. Jhansi Rani. "A Multiple-Layer Machine Learning Architecture for Improved Accuracy in Sentiment Analysis." Computer Journal 63, no. 3 (April 27, 2019): 395–409. http://dx.doi.org/10.1093/comjnl/bxz038.
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Abstract Twitter is an online micro-blogging platform through which one can explore the hidden valuable and delightful information about the current context at any point of time, which also serves as a data source to carry out sentiment analysis. In this paper, the sentiments of large amount of tweets generated from Twitter in the form of big data have been analyzed using machine learning algorithms. A multi-tier architecture for sentiment classification is proposed in this paper, which includes modules such as tokenization, data cleaning, preprocessing, stemming, updated lexicon, stopwords and emoticon dictionaries, feature selection and machine learning classifier. Unigram and bigrams have been used as feature extractors together with χ2 (Chi-squared) and Singular Value Decomposition for dimensionality reduction together with two model types (Binary and Reg), with four types of scaling methods (No scaling, Standard, Signed and Unsigned) and represented them in three different vector formats (TF-IDF, Binary and Int). Accuracy is considered as the evaluation standard for random forest and bagged trees classification methods. Sentiments were analyzed through tokenization and having several stages of pre-processing and several combinations of feature vectors and classification methods. Through which it was possible to achieve an accuracy of 84.14%. Obtained results conclude that, the proposed scheme gives a better accuracy when compared with existing schemes in the literature.
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Rew, D. A., and N. G. Popova. "The challenges of machine translation of academic publications." Science Editor and Publisher 6, no. 2 (November 2, 2021): 104–12. http://dx.doi.org/10.24069/sep-21-01.
Повний текст джерелаАнотація:
Clear translation remains a major challenge to better communication and understanding of the international academic literature, despite advances in Machine Translation (MT). Automatic translation systems which captured the detail and the sense of any manuscript in any language for a reader from any other linguistic background would find global applications.In this article, we discuss the current opportunities and constraints to the wider use of machine translation and computer-assisted human translation (CAT). At the present stage of technology development, these instruments offer a number of advantages to specialists working with scientific texts. These include the facility to skim and scan large amounts of information in foreign languages, and to act as digital dictionaries, thesauri and encyclopedias. Word-to-word and phrase-to-phrase translation between many languages and scripts is now well advanced.The availability of modern machine translation has therefore changed the work of specialist scientific translators, placing greater emphasis on more advanced text and sense editing skills. However, machine translation is still challenged by the nuances of language and culture from one society to another, particularly in the freestyle literature of the arts and humanities. Scientific papers are generally much more structured, but the quality of machine translation still largely depends on the quality of the source text. This varies considerably between different scientific disciplines and from one author to another.The most advanced translation systems are making steady progress. It is timely to revisit traditional training programmes in the field of written translation to focus on the development of higher-level research competencies, such as terminology search, and so to make best use of evolving machine translation technologies.More widely, we consider that there is a challenge across the higher education systems in all countries to develop a simple, clear and consistent “international” writing style to assist fast, reliable and low-cost machine translation and hence to advance mutual understanding across the global scientific literature.
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Zakharova, I. G. "Machine Learning Methods of Providing Informational Management Support for Students’ Professional Development." Education and science journal 20, no. 9 (December 4, 2018): 91–114. http://dx.doi.org/10.17853/1994-5639-2018-9-91-114.
Повний текст джерелаАнотація:
Introduction.Professional development of students requires effective interaction with teachers, scientists, university administrators, students, representatives of professional community and labour market. The effectiveness of this interaction resulted from its information support, based on reliable information, promptly provided to all the members of learning process.The aim of this paper was to study the machine learning methods potential for the effective management of learning process by the example of implementing information support component designed to diagnose and predict the professional development of students based on automatic text analysis.Methodology and research methods. The theoretical basis of the research involved modelling of students’ professional development using the analysis of textual informative and professional relevance in written works of students. To identify the characteristics of professional development, a computer cluster analysis of texts was carried out using the K-means method of clustering. The Bayes method was used to construct a model for classifying students from the standpoint of identified features.Results and scientific novelty. A computer analysis of texts relating to different stages of learning for the evaluation of general and special vocabulary was performed. Regularities in the dynamics of students’ use of general scientific and professional terminology were revealed. Accordingly, the groups with certain trends of educational behaviour of students were formed. It was shown how this differentiation, based on the complex of previously selected dynamic indicators characterising the changes of professional vocabulary, expands the possibilities for diagnostics and forecasting of professional growth of students. The author notes that the efficiency of similar intellectual systems is determined not only by the continued database up-dating, i.e. the amount of data in turn influence the accuracy of model of students’ classification and, consequently, the forecast of students’ professional development. Equally important is the improvement of knowledge base, which contains the criteria of professional development and complies with the requirement of basic dictionaries relevance. In addition, supportive procedures should be carried out with participating of the representatives of professional community.Practical significance. The information support provided for the management of professional development of students can be used both for operational decision making and developing content and technologies for educational process. This means students can evaluate the dynamics of own performance in comparison with earlier works, classmates’ work, target indicators of the use of general scientific and professional terminology. This information management component allows teachers to monitor the content of texts and easily determine the authorship of content of learner’s general frequency vocabulary and the dynamics of its change. The representatives of labour market along with access to information on the current progress of a student can define his or her prospects as a future worker. Heads of educational programmes, university administrators receive objective information about the content of disciplines as their study is reflected in the students’ professional development.
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Kholodna, N., and V. Vysotska. "TECHNOLOGY FOR GRAMMATICAL ERRORS CORRECTION IN UKRAINIAN TEXT CONTENT BASED ON MACHINE LEARNING METHODS." Radio Electronics, Computer Science, Control, no. 1 (February 27, 2023): 114. http://dx.doi.org/10.15588/1607-3274-2023-1-12.
Повний текст джерелаАнотація:
Context. Most research in grammatical and stylistic error correction focuses on error correction in English-language textual content. Thanks to the availability of large data sets, a significant increase in the accuracy of English grammar correction has been achieved. Unfortunately, there are few studies on other languages. Systems for the English language are constantly developing and currently actively use machine learning methods: classification (sequence tagging) and machine translation. A large amount of parallel or manually labelled data is required to build a high-quality machine learning model for correcting grammatical/stylistic errors in the texts of those morphologically complex languages. Manual data annotation requires a lot of effort by professional linguists, which makes the creation of text corpora, especially in morphologically rich languages, mainly Ukrainian, a time- and resource-consuming process.
Objective of the study is to develop a technology for correcting errors in Ukrainian-language texts based on machine learning methods using a small set of annotated parallel data.
Method. For this study, machine learning algorithms were selected when developing a system for correcting errors in Ukrainianlanguage texts using an optimal pipeline, including pre-processing and selecting text content and generating features in small annotated data corpora. The neural network’s use with a new architecture, a review of state-of-the-art methods, and a comparison of different pipeline stages will make it possible to determine such a combination of them, allowing a high-quality error correction model in Ukrainian-language texts.
Results. A machine learning model for error correction in Ukrainian-language texts has been developed. A universal scheme for creating an error correction system for different languages is proposed. According to the results, the neural network can correct simple sentences written in Ukrainian. However, creating a full-fledged system will require spell-checking using dictionaries and checking rules, both simple and based on the result of parsing dependencies or other features. The pre-trained neural translation model mT5 has the best performance among the three models. To save computing resources, it is also possible to use a pre-trained BERT-type neural network as an encoder and a decoder. Such a neural network has half the number of parameters as other pretrained machine translation models and shows satisfactory results in correcting grammatical and stylistic errors.
Conclusions. The created model shows excellent classification results on test data. The calculated machine translation quality metrics allow only a partial comparison of the models since most of the words and phrases in the original and corrected sentences are the same. The best value for both BLEU (0.908) and METEOR (0.956) is obtained for mT5, which is consistent with the case study in which the most accurate error corrections without changing the initial value of the sentence are obtained for such a neural network. The M2M100 has a higher BLEU score (0.847) than the “Ukrainian Roberta” Encoder-Decoder (0.697). However, subjectively evaluating the results of the correction of examples, the M2M100 does a much worse job than the other two models. For METEOR, M2M100 (0.925) also has a higher score than the “Ukrainian Roberta” Encoder-Decoder (0.876).
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Kong, Yun, Qinkai Han, and Fulei Chu. "Sparsity assisted intelligent recognition method for vibration-based machinery health diagnostics." Journal of Vibration and Control, July 11, 2022, 107754632211137. http://dx.doi.org/10.1177/10775463221113733.
Повний текст джерелаАнотація:
Vibration-based health diagnostics technique has shown great potentials to enhance the safety and reliability for many industrial rotary machinery. The emerging sparse representation classification (SRC) paradigm provides a promising tool for intelligent machinery health diagnostics. However, traditional SRC approaches neglect the useful priori information in rotary machinery vibration data, limiting the reconstruction ability and thus restricting their diagnostic accuracy. To address this issue, we present a novel sparsity assisted intelligent recognition (SAIR) methodology for vibration-based machinery health diagnostics. SAIR is constituted by two stages for dictionary design and intelligent recognition. In the dictionary design stage, SAIR exploits the useful priori information of the prediction shift-invariance property, to design class-specific dictionaries via an overlapping segmentation strategy. Additionally, this dictionary design strategy can leverage those local and nonlocal features within data segments. In the intelligent recognition stage, SAIR implements health state recognition by means of the sparsity-based health diagnostic strategy according to minimal sparse approximation errors. Finally, the feasibility and advantage of SAIR have been comprehensively evaluated for planetary gearbox health diagnostics, indicating that SAIR yields an overall diagnostic accuracy of 99.72%. Besides, comparative studies demonstrate that SAIR outperforms the advanced mainstream methods with better diagnostic accuracy and lower computation costs for vibration-based machinery health diagnostics.
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Dong, Guangming, Jin Chen, and Fagang Zhao. "Incipient Bearing Fault Feature Extraction Based on Minimum Entropy Deconvolution and K-Singular Value Decomposition." Journal of Manufacturing Science and Engineering 139, no. 10 (August 24, 2017). http://dx.doi.org/10.1115/1.4037419.
Повний текст джерелаАнотація:
Machinery condition monitoring and fault diagnosis are essential for early detection of equipment malfunctions or failures, which insure productivity, quality, and safety in the manufacturing process. This paper aims at extracting fault features of rolling element bearings at the incipient fault stage. K-singular value decomposition (K-SVD), one technique for sparse representation of signals, is used for study. In K-SVD, its dictionary is trained from data by machine learning techniques, which allows more flexibility to adapt to variation of real signals than the predefined dictionaries. Analysis on simulated bearing signals and real signals shows that K-SVD can give better bearing fault features than the predefined dictionaries such as wavelet dictionaries. However, during our simulation study, K-SVD was found to have large representation error under heavy noise. To reduce the noise effect, minimum entropy deconvolution (MED) is used as a prefilter. The combination of MED and K-SVD is proposed for incipient bearing fault detection. The method is verified by simulation and experimental study. It is shown that the proposed method can effectively extract the impulsive fault feature of the tested bearing at its incipient fault stage.
Книги з теми "Stage machinery – dictionaries"
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Roy, Alain. Dictionnaire raisonné et illustré du theátre à l'italienne. Paris: Fondation Beaumarchais, 1992.
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Alain, Roy. Dictionnaire raisonné et illustré du théâtre à l'italienne. Le Méjan, Arles: Actes sud-Papiers, 1992.
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3
Ladj, Michel. Le lexique de la scène. Paris: Editions AS, 1998.
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C, Boulanger Norman, ed. Theatre backstage from A to Z. 4th ed. Seattle: University of Washington Press, 1999.
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Norman, Boulanger, ed. Theatre backstage from A to Z. 3rd ed. Seattle: University of Washington Press, 1989.
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Anglo-russkiĭ slovarʹ teatralʹnykh tekhnicheskikh terminov. Moskva: British Council, 2001.
Знайти повний текст джерелаЧастини книг з теми "Stage machinery – dictionaries"
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Zaitseva, Еkaterina M., and Yury V. Smirnov. "Making terminology dictionary as an important element of library research activities." In Research in Llibrary theory and practice: Annual interdepartmental collection of scientific papers, 57–64. Russian National Public Library for Science and Technology, 2022. http://dx.doi.org/10.33186/978-5-85638-241-8-2022-57-64.
Повний текст джерелаАнотація:
The authors review the digital terminological dictionaries published by RNPLS&T. The publishing story is told; the importance of this work is substantiated, the publications are characterized. The terminological dictionaries, i.e. the English-Russian dictionary of machine-readable cataloguing, e-dictionary of SIBID terminology, e-dictionary of standardized abbreviations in Russian and 25 European languages for bibliographic records, and the union terminology dictionary of e-librarianship, are described in brief. In conclusion, the further plan for the Library’s linguistic support group is discussed as related to creating terminological dictionaries. The article is prepared within the framework of the State Order No. 730000F99.1 BV09AA00006 to RNPLS&T.
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Atkinson, M. D., and D. Tulley. "The Combinatorics of some Abstract Data Types." In Applications of Combinatorial Mathematics, 1–14. Oxford University PressOxford, 1997. http://dx.doi.org/10.1093/oso/9780198511922.003.0001.
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Abstract Abstract data types (ADTs) may be regarded as abstract machines and then a program for an ADT is any sequence of operations allowed by its speciflcation. The effect of such programs on container ADTs is captured by the relationship between each input sequence and the set of possible output sequences that can result from it. This relationship is studied principally in the case of dictionaries, stacks and priority queues and a distinction is drawn between ADTs of unbounded and bounded capacity. Abstract machines have a long and honourable history in Computer Science. Turing machines, push-down automata, and flnite-state machines are three well known types; they have been used to study general purpose computers, compilers, and string manipulation although their importance goes well beyond these three applications. The central theoretical issue for these (and other) machines is the characterisation of the languages associated with them. The aim of this paper is to study some abstract data types in the same spirit.
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Van Le, Duy, James Montgomery, Kenneth Kirkby, and Joel Scanlan. "Using Natural Language Processing to Predict Risk in Electronic Health Records." In Studies in Health Technology and Informatics. IOS Press, 2024. http://dx.doi.org/10.3233/shti231030.
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Clinical narratives recording behaviours and emotions of patients are available from EHRs in a forensic psychiatric centre located in Tasmania. This rich data has not been used in risk prediction. Prior work demonstrates natural language processing can be used to identify patient symptoms in these free-text records and can then be used to predict risk. Four dictionaries containing descriptive words of harm were created using the Diagnostic and Statistical Manual of Mental Disorders, the Unified Medical Language System repository, English negative and positive sentiment words, and high-frequency words from the Corpus of Contemporary American English. However, a model based only on these keywords is limited in predictive power. In this study, we introduce an improved NLP approach with a social interaction component to extract additional information about the behavioural and emotional state of patients. These social interactions are subsequently used in a machine-learning model to enhance risk prediction performance.
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Kumar Reddy, K. Ravi, K. Kailash, and Y. Vani. "Artificial General Intelligence; Pragmatism or an Antithesis?" In Artificial Intelligence and Knowledge Processing: Methods and Applications, 1–25. BENTHAM SCIENCE PUBLISHERS, 2023. http://dx.doi.org/10.2174/9789815165739123010004.
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Artificial intelligence is promoted by means of incomprehensible advocacy through business majors that cannot easily be equated with human consciousness and abilities. Behavioral natural systems are quite different from language models and numeric inferences. This paper reviews through centuries of evolved human knowledge, and the resolutions as referred through the critics of mythology, literature, imagination of celluloid, and technical work products, which are against the intellect of both educative and fear mongering. Human metamorphic abilities are compared against the possible machine takeover and scope of envisaged arguments across both the worlds of ‘Artificial Intelligence’ and ‘Artificial General Intelligence’ with perpetual integrations through ‘Deep Learning’ and ‘Machine Learning’, which are early adaptive to ‘Artificial Narrow Intelligence’ — a cross examination of hypothetical paranoid that is gripping humanity in modern history. The potentiality of a highly sensitive humanoid and sanctification to complete consciousness at par may not be a near probability, but social engineering through the early stages in life may indoctrinate biological senses to a much lower level of ascendancy to Artificial Narrow Intelligence — with furtherance in swindling advancement in processes may reach to a pseudo-Artificial Intelligence {i}. There are no convincing answers to the discoveries from ancient scriptures about the consciousness of archetypal humans against an anticipated replication of a fulfilling Artificial Intelligence {ii}. Human use of lexicon has been the focal of automata for the past few years and the genesis for knowledge, and with the divergence of languages and dialects, scores of dictionaries and tools that perform bidirectional voice and text — contextual services are already influencing the lives, and appeasement to selective humanly incidentals is widely sustainable today {iii}. Synthesizing and harmonizing a pretentious labyrinthine gizmo is the center of human anxiety, but only evaluative research could corroborate that tantamount to genetic consciousness.
Тези доповідей конференцій з теми "Stage machinery – dictionaries"
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Zaitseva, Ekaterina, and Yury Smirnov. "Electronic terminological dictionaries published by the Russian National Public Library for Science and Technology and plans for the future." In Sixth World Professional Forum "The Book. Culture. Education. Innovations". Russian National Public Library for Science and Technology, 2021. http://dx.doi.org/10.33186/978-5-85638-236-4-2021-110-114.
Повний текст джерелаАнотація:
Russian National Public Library for Science and Technology publishes electronic terminological dictionaries. The history of this activity line and the publications are discussed, e. g.: The English-Russian Dictionary in Machine-readable Cataloguing, SIBID Electronic Terminological Dictionary (1-st and 2-nd editions), and Electronic Dictionary of Standardized Abbreviations in the Russian and 25 Foreign European Languages for Bibliographic Records. Links to purchase the dictionaries are given. Plans for the future are discussed. The paper is prepared within the framework of the State Order to RNPLS&T for 2021 No. 730000F.99.1.BV09АА00006.
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Cao, Zhanhao. "A Context-Aware Vocabulary Management and Reading Assistance System using Machine Learning and Natural Language Processing." In 8th International Conference on Artificial Intelligence (ARIN 2022). Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.121007.
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Through the increase in the popularity of online reading, many people rely on online dictionaries to further understand the text [1]. However, looking up a word manually is a great inconvenience as well as a form of distraction [2]. This paper develops a chrome extension to automatically detect the difficult words for each user, and provide the words’ associated definition with a mouse hover. The chrome extension can be customized by adding and removing personal difficult words and personal easy words [3]. Also, the chrome extension offers a deeper level of analytic, including the system analyzing part of speech of the world, to further understand the definition of a selected word or sentence. The chrome extension is applied to a school/work setting in order to improve the working efficiency by providing a simple model to analyze the word definition; it is also useful for casual reading, especially to those that aren’t fluent in English. Following the strict SDLC model, the end of the testing stage reflects that most of the users gave positive feedback to the chrome extension with most of the comments centered around convenience and accuracy [4]. Through alpha testing and a small sample of beta testing, the Chrome extension presents productivity improvement on difficult texts.