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Статті в журналах з теми "Computer dictionary"

Автор: Grafiati

Опубліковано: 30 травня 2022

Оновлено: 31 травня 2022

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1

Idham, Farid, and Ona Maliki. "Aplikasi Kamus Istilah Komputer Pada Perangkat Mobile Menggunakan fitur Voice, Abjad dan Kategori Berbasis Android." JSAI (Journal Scientific and Applied Informatics) 5, no. 1 (February 2, 2022): 12–19. http://dx.doi.org/10.36085/jsai.v5i1.2778.

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Анотація:

The dictionary, the term computer widely circulated today, is still in the form of books. Many make it difficult for users because they have to search for the meaning and terms of the computer manually. The method used in this research using the Research and Development method, where the researcher conducts fact gathering that refers to the KBBI dictionary. It was then built using the android studio application as a medium to create an information system term computer dictionary.Â With this Android-based dictionary, anyone and anywhere anyone can access it easily. For that, there is a need for a dictionary of terms, computers to facilitate the public in understanding the meaning of words and terms of computers in the form of mobile applications, especially android-based.

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2

Lydiatt, Kathryn, and M. F. Ghanayem. "Arabic Computer Dictionary." Arab Law Quarterly 2, no. 4 (November 1987): 425. http://dx.doi.org/10.2307/3381614.

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3

Wyatt, Allen L. "Computer Professional's Dictionary." Social Science Computer Review 9, no. 2 (July 1991): 358. http://dx.doi.org/10.1177/089443939100900228.

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4

Deeson, Eric. "Dictionary of Computer Graphics." Physics Bulletin 36, no. 6 (June 1985): 260. http://dx.doi.org/10.1088/0031-9112/36/6/029.

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5

Sim, Richard. "Dictionary of computer graphics." Microprocessors and Microsystems 9, no. 4 (May 1985): 201–2. http://dx.doi.org/10.1016/0141-9331(85)90009-2.

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6

Handayani, Dwipa, and Abrar Hiswara. "KAMUS ISTILAH ILMU KOMPUTER DENGAN ALGORITMA BOYER MOORE BERBASIS WEB." Jurnal Informatika 19, no. 2 (December 26, 2019): 90–97. http://dx.doi.org/10.30873/ji.v19i2.1519.

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Анотація:

A dictionary is a reference book that contains words and phrases that are usually arranged in alphabetical order along with an explanation of their meaning, usage and translation and function to help recognize new terms. The field of computer science certainly has specific terms related to computers, so it is needed a dictionary of computer terms, currently the existing dictionary is still conventional in its use ineffective and inefficient. The design and manufacture of applications using algorithms by performing a sequence of logical steps in solving problems that are arranged systematically. Algorithms for searching are now growing day by day. Boyer Moore algorithm is one of the search algorithms that is considered to have the best results, namely the algorithm that moves matching strings from right to left. With this web-based dictionary the user is expected to be able to get information quickly, without any limitations on space and time. Keywords: Boyer Moore's Algorithm, Computer Science, Glossary of Terms, Web.

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7

Geraci, A. "The Computer Dictionary Project: an update." Computer 29, no. 7 (July 1996): 95. http://dx.doi.org/10.1109/2.511973.

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8

DROZHASHCHIKH, Nataliya V., and Anastasiya Yu BASHMAKOVA. "MULTIMODAL LEARNER’S DICTIONARY WITH HISTORICAL AND CULTURAL COMPONENTS IN EFL CLASSROOM." Tyumen State University Herald. Humanities Research. Humanitates 7, no. 1 (2021): 43–61. http://dx.doi.org/10.21684/2411-197x-2021-7-1-43-61.

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Анотація:

This article studies the problem of making a multimodal computer dictionary with historical and cultural components for EFL classrooms. The relevance of the research topic is associated with the development of computer lexicography as a branch of applied linguistics at the intersection with digital humanities and digital pedagogy, as well as with the active development of new methods and tools in the practice of teaching foreign languages. The object of the research is a multimodal learners’ computer dictionary that visualizes semantic changes in lexicon of the English language, in particular, the key lexemes of the thematic field “еducation”. The purpose of this article is to substantiate the problems of creating an educational multimodal dictionary of a historical and cultural type in computer lexicography. One of the objectives of the research is to study the specific semantic features of the units in the thematic field “education” in the multimodal learner’s dictionary. The multimodal dictionary plays an important role in the interaction of the user with the virtual system, where the dictionary is not only an additional reference book but also a multifunctional means of teaching and obtaining information about the culture of the target language countries, including the dynamics of its development. The introduction of such dictionaries into teaching is the key to successful immersion in the culture of the target language country and mastering language competencies. This article presents the elements of typology and principles of building computer dictionaries, notes the role of a multimodal computer dictionary in teaching a foreign language, and proposes a model of a learner’s multimodal dictionary of historical and cultural type for EFL classroom, as well as outlines the stages of dynamic development in English vocabulary.

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9

Allen, Roger, and Ernest Kay. "Arabic Computer Dictionary: English/Arabic, Arabic/English." Modern Language Journal 72, no. 1 (1988): 78. http://dx.doi.org/10.2307/327582.

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10

Fisher, Robert. "Dictionary of Computer Vision and Image Processing." Journal of Electronic Imaging 15, no. 1 (July 1, 2006): 019902. http://dx.doi.org/10.1117/1.2179077.

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11

Izenstark, Amanda. "A Dictionary of Computer Science (7th edition)." Reference Reviews 31, no. 5 (June 19, 2017): 24–25. http://dx.doi.org/10.1108/rr-02-2017-0041.

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12

van der Linden, Elisabeth, and Koenraad De Smedt. "Computerlexica Voor een Auteursysteem." Toegepaste Taalwetenschap in Artikelen 27 (January 1, 1987): 33–41. http://dx.doi.org/10.1075/ttwia.27.05lin.

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Анотація:

The accessibility of lexical information stored on computers is not only important for the human computer user, but also for programs that process natural language. The requirements with respect to the content and structure of a computer dictionary are different than for a printed dictionary and depend on the specific function of the language processing system. For example, the lexical knowledge needed for hyphenation is different from that for the correction of spelling errors. A number of such language manipulating modules which are designed in the context of the Nijmegen Language Technology project are discussed with special attention to the dictionaries which they use. If one tries to integrate the different functions of an author system, integration of the different kinds of lexical knowledge in one database is an obvious task to consider. When designing a computer lexicon with many kinds of information, the redundant information which one finds in a conventional dictionary can be extracted and represented in the form of abstract categories and rules. Taking that approach not only makes the lexicon more compact but also makes lexical concepts explicit and usable in different ways.

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13

ZHUSSUPOVA, B. T., D. ZH ALIPPAYEVA, and S. A. KUDUBAYEVA. "Dictionaries of the kazakh language as the basis of semantic analysis in the system of computer sign language translation." Bulletin of the National Engineering Academy of the Republic of Kazakhstan 4, no. 78 (January 10, 2020): 71–75. http://dx.doi.org/10.47533/2020.1606-146x.34.

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Анотація:

his article discusses the development of a semantic dictionary of the Kazakh language for a computer translation system from Kazakh to Kazakh sign language, which will take into account the semantics of the Kazakh language and the Kazakh sign language. The semantic dictionary of the Kazakh language serves as the basis of computer translation technology from the Kazakh language to the Kazakh sign language. In the future, it will allow semantic analysis of the source text. The authors of the article analyzed and selected the available dictionaries of the Kazakh language used in the development of the semantic dictionary database. Dictionaries of the Kazakh language provide an opportunity for computer-based sign language translation of the Kazakh sign language. The article also presents the possibility of using L. S. Dimskis notation to develop a dictionary of the structure of gestures of the Kazakh sign language. The prospect of its inclusion in the database of semantic dictionary is revealed. And also revealed the need for a dictionary of gestures in the development of automated sign language translation system as a whole, taking into account its effectiveness and the possibility of full practical use.

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14

ZHUSSUPOVA, B. T., D. ZH ALIPPAYEVA, and S. A. KUDUBAYEVA. "Dictionaries of the kazakh language as the basis of semantic analysis in the system of computer sign language translation." Bulletin of the National Engineering Academy of the Republic of Kazakhstan 4, no. 78 (January 10, 2020): 71–75. http://dx.doi.org/10.47533/2020.1606-146x.34.

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Анотація:

his article discusses the development of a semantic dictionary of the Kazakh language for a computer translation system from Kazakh to Kazakh sign language, which will take into account the semantics of the Kazakh language and the Kazakh sign language. The semantic dictionary of the Kazakh language serves as the basis of computer translation technology from the Kazakh language to the Kazakh sign language. In the future, it will allow semantic analysis of the source text. The authors of the article analyzed and selected the available dictionaries of the Kazakh language used in the development of the semantic dictionary database. Dictionaries of the Kazakh language provide an opportunity for computer-based sign language translation of the Kazakh sign language. The article also presents the possibility of using L. S. Dimskis notation to develop a dictionary of the structure of gestures of the Kazakh sign language. The prospect of its inclusion in the database of semantic dictionary is revealed. And also revealed the need for a dictionary of gestures in the development of automated sign language translation system as a whole, taking into account its effectiveness and the possibility of full practical use.

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15

Leslie, W. H. P. "Graphics dictionary." Computer-Aided Design 17, no. 1 (January 1985): 47. http://dx.doi.org/10.1016/0010-4485(85)90013-2.

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16

Cassidy, Frederic G. "The fallible computer and DARE." English Today 3, no. 1 (January 1987): 27–30. http://dx.doi.org/10.1017/s0266078400002674.

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Анотація:

The Dictionary of American Regional English is not only a great national scholarly project but also a pioneering venture in lexicography. How did dialectology and the computer work out together in the preparation of Volume I?

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17

Kikuchi, Hiroto, Hong Shen, and S. Iwasaki. "Processing Speed Acceleration of an Intelligent Communication Instrument." Key Engineering Materials 381-382 (June 2008): 639–42. http://dx.doi.org/10.4028/www.scientific.net/kem.381-382.639.

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Анотація:

This paper makes an attempt to improve the retrieving algorithm for an intelligent communication instrument. The instrument is developed for visually impaired computer users, and it converts texts displayed on a computer screen into audio messages to help them operate the computer. The conversion is performed based on a user dictionary, and a retrieving algorithm is needed to search the dictionary. In order to accelerate the retrieving speed, we adopt an index table when retrieving the first two alphabets of a word in the dictionary. The time for retrieving words is measured in the experiments. The experimental results reveal that the retrieving operations can be finished within an acceptable time.

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18

Cmejrek, Martin, and Jan Curín. "Automatic Extraction of Terminological Translation Lexicon from Czech-English Parallel Texts." Text Corpora and Multilingual Lexicography 6, no. 3 (December 17, 2001): 1–12. http://dx.doi.org/10.1075/ijcl.6.si.02cme.

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Анотація:

We present experimental results of an automatic extraction of a Czech-English translation dictionary. Two different bilingual corpora (119,886 sentence pairs computer-oriented and 58,137 journalistic corpora) were created. We used the length-based statistical method for sentence alignment (Gale and Church 1991) and noun phrase marker working with regular grammar and probabilistic model (Brown et al. 1993) for dictionary extraction. Resulting dictionaries’ size varies around 6,000 entries. After significance filtering, weighted precision is 86.4% for computer-oriented and 70.7% for journalistic Czech-English dictionary.

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19

Wakin, Jeanette, and Wilson B. Bishai. "A Computer Dictionary of Literary Arabic: Arabic-English." Journal of the American Oriental Society 110, no. 4 (October 1990): 776. http://dx.doi.org/10.2307/602934.

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20

Larson, Jerry W., and Alfredo Chiri. "Spanish-English/English-Spanish Dictionary of Computer Terms." Modern Language Journal 78, no. 3 (1994): 414. http://dx.doi.org/10.2307/330153.

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21

RODRIGUEZ, C. "A Computer Implementation of a Spanish Synonym Dictionary." Literary and Linguistic Computing 4, no. 4 (October 1, 1989): 265–70. http://dx.doi.org/10.1093/llc/4.4.265.

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22

Radatz, J. "A standard dictionary for computer terminology: Project 610." Computer 21, no. 2 (February 1988): 72–77. http://dx.doi.org/10.1109/2.20.

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23

Charalampopoulos, Panagiotis, Tomasz Kociumaka, Manal Mohamed, Jakub Radoszewski, Wojciech Rytter, and Tomasz Waleń. "Internal Dictionary Matching." Algorithmica 83, no. 7 (April 17, 2021): 2142–69. http://dx.doi.org/10.1007/s00453-021-00821-y.

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24

Hoang Vu, Nguyen, Tran Quoc Cuong, and Tran Thanh Phong. "DISCRIMINATIVE DICTIONARY PAIR LEARNING FOR IMAGE CLASSIFICATION." Journal of Computer Science and Cybernetics 36, no. 4 (December 14, 2020): 347–63. http://dx.doi.org/10.15625/1813-9663/36/4/15105.

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Анотація:

Dictionary learning (DL) for sparse coding has been widely applied in the field of computer vision. Many DL approaches have been developed recently to solve pattern classification problems and have achieved promising performance. In this paper, to improve the discriminability of the popular dictionary pair learning (DPL) algorithm, we propose a new method called discriminative dictionary pair learning (DDPL) for image classification. To achieve the goal of signal representation and discrimination, we impose the incoherence constraints on the synthesis dictionary and the low-rank regularization on the analysis dictionary. The DDPL method ensures that the learned dictionary has the powerful discriminative ability and the signals are more separable after coding. We evaluate the proposed method on benchmark image databases in comparison with existing DL methods. The experimental results demonstrate that our method outperforms many recently proposed dictionary learning approaches.

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25

Makarych, M. V., Y. B. Popova, and M. O. Shved. "Linguistic database and software for english-belarusian-russian dictionary of technical terms." «System analysis and applied information science», no. 4 (February 6, 2019): 74–82. http://dx.doi.org/10.21122/2309-4923-2018-4-74-82.

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Анотація:

The central object of computer lexicography is a computer or electronic dictionary, which must have a sufficiently large vocabulary, provide the consistent extraction of information depending on the user’s need and provide complete grammatical information about the words of input and output languages. Taking into account the current trend in the development of special terminological dictionaries, the authors propose an English-Belarusian-Russian dictionary of technical terms. At the initial stage of the work the dictionary was named TechLex and covers the following subject areas: architecture and construction, water supply, information technology, pedagogy, transport communications, economics, energy-supply. Currently, each subject area of the dictionary is located in the Internet GoogleTable and contains about 1000 terms. It has the possibility to be simultaneously filled by several teachers. The linguistic database of the dictionary is not created by the traditional way of processing a large number of paper dictionaries and combining the received translations. Lexis from sequential processing of scientific and technical English periodicals of particular subject areas is the base of it. The software of the proposed electronic dictionary is designed taking into account the analysis of modern electronic multilingual translation dictionaries and is a client-server application in Java programming language. The client part of the system contains a mobile application for the Android operating system, which was tested on tablets and smartphones with different screen diagonals. The interface of TechLex dictionary is designed in such a way that only a single zone is activated according to the query, so there is no need to view all the subject areas of the dictionary. The proposed TechLex dictionary is the first technical multilingual electronic dictionary with an English-Belarusian-Russian version.

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26

Zhou, Jian, and Jianli Liu. "Segmentation of defects in textile fabric with robust texture representation and total variation." International Journal of Clothing Science and Technology 32, no. 6 (April 28, 2020): 813–23. http://dx.doi.org/10.1108/ijcst-10-2019-0157.

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Анотація:

PurposeVisual quality control on raw textile fabrics is a vital process in weaving factories to ensure their exterior quality (visual defects or imperfection) satisfying customer requirements. Commonly, this critical process is manually conducted by human inspectors, which can hardly provide a fast and reliable inspection results due to fatigue and subjective errors. To meet modern production needs, it is highly demanded to develop an automated defect inspection system by replacing human eyes with computer vision.Design/methodology/approachAs a structural texture, fabric textures can be effectively represented by a linearly summation of basic elements (dictionary). To create a robust representation of a fabric texture in an unsupervised manner, a smooth constraint is imposed on dictionary learning model. Such representation is robust to defects when using it to recover a defective image. Thus an abnormal map (likelihood of defective regions) can be computed by measuring similarity between recovered version and itself. Finally, the total variation (TV) based model is built to segment defects on the abnormal map.FindingsDifferent from traditional dictionary learning method, a smooth constraint is introduced in dictionary learning that not only able to create a robust representation for fabric textures but also avoid the selection of dictionary size. In addition, a TV based model is designed according to defects' characteristics. The experimental results demonstrate that (1) the dictionary with smooth constraint can generate a more robust representation of fabric textures compared to traditional dictionary; (2) the TV based model can achieve a robust and good segmentation result.Originality/valueThe major originality of the proposed method are: (1) Dictionary size can be set as a constant instead of selecting it empirically; (2) The total variation based model is built, which can enhance less salient defects, improving segmentation performance significantly.

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27

Nikitina, Tatiana, and Elena Rogaleva. "Innovative Approaches to Creating Training Phraseological Dictionary as Interactive Hypertext." SOCIETY, INTEGRATION, EDUCATION. Proceedings of the International Scientific Conference 2 (May 17, 2015): 175. http://dx.doi.org/10.17770/sie2015vol2.446.

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Анотація:

<p><em>The article reviews material on Russian training phraseography, in particular, it contains an analysis of the disadvantages of the interpretation of idioms in phraseological dictionaries for primary school learners. The author presents her own technology of creating an innovative phraseological dictionary for primary school children which combines traditional principles for describing idioms in phraseological dictionaries with stimulation of an interactive dialogue between the author and the young reader. As a result, the interactive hypertext of a non-computer dictionary approximates to an electronic dictionary and an educational computer game that satisfies primary school learners’ demand and facilitates children’s learning idioms.</em></p>

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28

Wang, Ying. "Optimization of English Online Learning Dictionary System Based on Multiagent Architecture." Complexity 2021 (April 29, 2021): 1–10. http://dx.doi.org/10.1155/2021/1994121.

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Анотація:

As a universal language in the world, English has become a necessary language communication tool under the globalization of trade. Intelligent, efficient, and reasonable English language-assisted learning system helps to further improve the English ability of language learners. English online learning dictionary, as an important query tool for English learners, is an important part of English online learning. This paper will optimize the design of English online learning dictionary system based on multiagent architecture. Based on the hybrid multiagent cooperative algorithm, this paper will improve the disadvantages of the online English learning dictionary system and propose an appropriate dictionary application evaluation function. At the same time, an improved reinforcement learning algorithm is introduced into the corresponding English online learning dictionary navigation problem so as to improve the efficiency of the online English learning dictionary system. English online learning dictionary is more intelligent and efficient. In this paper, the new online learning dictionary system optimization algorithm is proposed and compared with the traditional system algorithm. The experimental results show that the algorithm proposed in this paper solves the collaborative confusion problem of English learning online dictionary to a certain extent and further solves the corresponding navigation problem so as to improve the efficiency.

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29

Lu, Cewu, Jianping Shi, and Jiaya Jia. "Scale Adaptive Dictionary Learning." IEEE Transactions on Image Processing 23, no. 2 (February 2014): 837–47. http://dx.doi.org/10.1109/tip.2013.2287602.

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30

Shenghua Gao, Ivor Wai-Hung Tsang, and Yi Ma. "Learning Category-Specific Dictionary and Shared Dictionary for Fine-Grained Image Categorization." IEEE Transactions on Image Processing 23, no. 2 (February 2014): 623–34. http://dx.doi.org/10.1109/tip.2013.2290593.

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31

Liang, Bo, Xin-xin Jia, and Yuan Lu. "Application of Adaptive Image Restoration Algorithm Based on Sparsity of Block Structure in Environmental Art Design." Complexity 2021 (May 26, 2021): 1–16. http://dx.doi.org/10.1155/2021/9035163.

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Анотація:

Image restoration is a research hotspot in computer vision and computer graphics. It uses the effective information in the image to fill in the information of the designated damaged area. This has high application value in environmental design, film and television special effects production, old photo restoration, and removal of text or obstacles in images. In traditional sparse representation image restoration algorithms, the size of dictionary atoms is often fixed. When repairing the texture area, the dictionary atom will be too large to cause blurring. When repairing a smooth area, the dictionary atom is too small to cause the extension of the area, which affects the image repair effect. In this paper, the structural sparsity of the block to be repaired is used to adjust the repair priority. By analyzing the structure information of the repair block located in different regions such as texture, edge, and smoothing, the size of the dictionary atom is adaptively determined. This paper proposes a color image restoration method that adaptively determines the size of dictionary atoms and discusses a model based on the partial differential equation restoration method. Through simulation experiments combined with subjective and objective standards, the repair results are evaluated and analyzed. The simulation results show that the algorithm can effectively overcome the shortcomings of blurred details and region extension in fixed dictionary restoration, and the restoration effect has been significantly improved. Compared with the results of several other classic algorithms, it shows the effectiveness of the algorithm in this paper.

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32

Pollard, W. F., and Tony Gunton. "The Penguin Dictionary of Information Technology and Computer Science." Mathematical Gazette 78, no. 481 (March 1994): 78. http://dx.doi.org/10.2307/3619450.

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33

MITTON, R. "A Partial Dictionary of English in Computer-Usable Form." Literary and Linguistic Computing 1, no. 4 (October 1, 1986): 214–15. http://dx.doi.org/10.1093/llc/1.4.214.

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34

Vitale, Anthony J. "Computer apparatus for text-to-speech synthesizer dictionary reduction." Journal of the Acoustical Society of America 112, no. 2 (2002): 369. http://dx.doi.org/10.1121/1.1507009.

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35

Yokoi, Toshio. "The EDR electronic dictionary." Communications of the ACM 38, no. 11 (November 1995): 42–44. http://dx.doi.org/10.1145/219717.219752.

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36

Neuburger, Shoshana, and Dina Sokol. "Succinct 2D Dictionary Matching." Algorithmica 65, no. 3 (February 2, 2012): 662–84. http://dx.doi.org/10.1007/s00453-012-9615-9.

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37

Wang, Rui, Miaomiao Shen, Tao Wang, and Wenming Cao. "Multi-task hybrid dictionary learning for vehicle classification in sensor networks." International Journal of Distributed Sensor Networks 14, no. 11 (November 2018): 155014771880902. http://dx.doi.org/10.1177/1550147718809020.

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Анотація:

In this article, we propose a novel multi-task hybrid dictionary learning approach for moving vehicle classification tasks using multi-sensor networks to improve the classification accuracy in complex scenes with low time complexity, which considers both correlations and complementary information among multiple heterogeneous sensors simultaneously to learn a hybrid dictionary within observations of each sensor. The efficient hybrid dictionary consists of a synthesis dictionary and an analysis dictionary, where discriminative codes can be generated by the trained analysis dictionary and class-specific discriminative reconstruction can be achieved by the trained synthesis dictionary. Extensive experiments are conducted on real data sets captured by the multiple heterogeneous sensors, and the results demonstrate that the proposed method can use the multi-feature fusion method to improve the vehicle classification accuracy, and it can learn a hybrid dictionary to make sure that the sparse coding matrix is obtained by simple linear mapping function. Moreover, the problem of [Formula: see text]-norm[Formula: see text] sparse coding can been solved, to reduce the time complexity of this algorithm, compared with support vector machine, sparse representation classification, label consistent KSVD, Fisher discrimination dictionary learning, hybrid dictionary learning, multi-task sparse representation classification, and multi-task Fisher discrimination dictionary learning algorithms.

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38

Amir, Amihood, Martin Farach, Zvi Galil, Raffaele Giancarlo, and Kunsoo Park. "Dynamic dictionary matching." Journal of Computer and System Sciences 49, no. 2 (October 1994): 208–22. http://dx.doi.org/10.1016/s0022-0000(05)80047-9.

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39

Aktaş, Yeşim, and Abdulkadir Çakır. "Conceptual analysis studies in Turkish tests and suggestions to solve the computer network terms lexicon." Global Journal of Information Technology: Emerging Technologies 7, no. 1 (June 27, 2017): 2–7. http://dx.doi.org/10.18844/gjit.v7i1.1932.

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Анотація:

AbstractConceptual inference on texts with natural language processing is one of the most important topics of artificial intelligence. The meaning extraction process with artificial intelligence algorithms is frequently done in foreign languages, and in recent years studies for Turkish have been increasing. In this study, concept-meaning extraction studies made for Turkish have been researched and extraction algorithms which can be done on the computer network concept dictionary have been investigated. Keywords: artificial intelligence, wordnet, algorithms, dictionary, network.

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40

Turan, Erhan, and Umut Orhan. "Confidence Indexing of Automated Detected Synsets: A Case Study on Contemporary Turkish Dictionary." ACM Transactions on Asian and Low-Resource Language Information Processing 21, no. 1 (January 31, 2022): 1–19. http://dx.doi.org/10.1145/3469724.

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In this study, a novel confidence indexing algorithm is proposed to minimize human labor in controlling the reliability of automatically extracted synsets from a non-machine-readable monolingual dictionary. Contemporary Turkish Dictionary of Turkish Language Association is used as the monolingual dictionary data. First, the synonym relations are extracted by traditional text processing methods from dictionary definitions and a graph is prepared in Lemma-Sense network architecture. After each synonym relation is labeled by a proper confidence index, synonym pairs with desired confidence indexes are analyzed to detect synsets with a spanning tree-based method. This approach can label synsets with one of three cumulative confidence levels (CL-1, CL-2, and CL-3). According to the confidence levels, synsets are compared with KeNet which is the only open access Turkish Wordnet. Consequently, while most matches with the synsets of KeNet is determined in CL-1 and CL-2 confidence levels, the synsets determined at CL-3 level reveal errors in the dictionary definitions. This novel approach does not find only the reliability of automatically detected synsets, but it can also point out errors of detected synsets from the dictionary.

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41

Wang, Yuanyun, Chengzhi Deng, Jun Wang, Wei Tian, and Shengqian Wang. "Robust Visual Tracking with Discrimination Dictionary Learning." Advances in Multimedia 2018 (September 2, 2018): 1–10. http://dx.doi.org/10.1155/2018/7357284.

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It is a challenging issue to deal with kinds of appearance variations in visual tracking. Existing tracking algorithms build appearance models upon target templates. Those models are not robust to significant appearance variations due to factors such as illumination variations, partial occlusions, and scale variation. In this paper, we propose a robust tracking algorithm with a learnt dictionary to represent target candidates. With the learnt dictionary, a target candidate is represented with a linear combination of dictionary atoms. The discriminative information in learning samples is exploited. In the meantime, the learning processing of dictionaries can learn appearance variations. Based on the learnt dictionary, we can get a more stable representation for target candidates. Additionally, the observation likelihood is evaluated based on both the reconstruct error and dictionary coefficients with l1 constraint. Comprehensive experiments demonstrate the superiority of the proposed tracking algorithm to some state-of-the-art tracking algorithms.

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42

Handrizal, Handrizal, Andri Budiman, and Desy Rahayu Ardani. "Implementation and Analysis Zhu-Takaoka Algorithm and Knuth-Morris-Pratt Algorithm for Dictionary of Computer Application Based on Android." IJISTECH (International Journal Of Information System & Technology) 1, no. 1 (November 13, 2017): 8. http://dx.doi.org/10.30645/ijistech.v1i1.2.

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Анотація:

The string matching algorithm is the one of the most important parts in the various processes related to data and text types, which is the word search on computer dictionary. Computers have a basic role in the field of education, especially in teaching and learning activities. So that the classical learning model, that is by using the book as learning resource can be boring. To make it easier for users who searching words, we made an offline dictionary application based on Android by applying Zhu-Takaoka algorithm and Knuth-Morris-Pratt algorithm. The performance of Zhu-Takaoka is doing a search starts from the end of pattern that is tailored to the text, but in Knuth-Morris-Pratt algorithm starts from the beginning of pattern till match which the pattern used is word searched. The result of this research indicates that the Zhu-Takaoka algorithm is faster than the Knuth-Morris-Pratt algorithm which showed the running time of each algorithm.

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43

Clément, Julien, Jean-Pierre Duval, Giovanna Guaiana, Dominique Perrin, and Giuseppina Rindone. "Parsing with a finite dictionary." Theoretical Computer Science 340, no. 2 (June 2005): 432–42. http://dx.doi.org/10.1016/j.tcs.2005.03.030.

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44

Zeng, Shaoning, Bob Zhang, Jianping Gou, Yong Xu, and Wei Huang. "Fast and Robust Dictionary-based Classification for Image Data." ACM Transactions on Knowledge Discovery from Data 15, no. 6 (May 19, 2021): 1–22. http://dx.doi.org/10.1145/3449360.

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Dictionary-based classification has been promising in knowledge discovery from image data, due to its good performance and interpretable theoretical system. Dictionary learning effectively supports both small- and large-scale datasets, while its robustness and performance depends on the atoms of the dictionary most of the time. Empirically, using a large number of atoms is helpful to obtain a robust classification, while robustness cannot be ensured when setting a small number of atoms. However, learning a huge dictionary dramatically slows down the speed of classification, which is especially worse on the large-scale datasets. To address the problem, we propose a Fast and Robust Dictionary-based Classification (FRDC) framework, which fully utilizes the learned dictionary for classification by staging - and -norms to obtain a robust sparse representation. The new objective function, on the one hand, introduces an additional -norm term upon the conventional -norm optimization, which generates a more robust classification. On the other hand, the optimization based on both - and -norms is solved in two stages, which is much easier and faster than current solutions. In this way, even when using a limited size of dictionary, which makes sure the classification runs very fast, it still can gain higher robustness for multiple types of image data. The optimization is then theoretically analyzed in a new formulation, close but distinct to elastic-net, to prove it is crucial to improve the performance under the premise of robustness. According to our extensive experiments conducted on four image datasets for face and object classification, FRDC keeps generating a robust classification no matter whether using a small or large number of atoms. This guarantees a fast and robust dictionary-based image classification. Furthermore, when simply using deep features extracted via some popular pre-trained neural networks, it outperforms many state-of-the-art methods on the specific datasets.

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45

Loeb, A. L., and E. J. Applewhite. "Synergetics Dictionary." Leonardo 20, no. 1 (1987): 98. http://dx.doi.org/10.2307/1578224.

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46

Tait, Kenneth. "Dictionary of the Internet." Journal of Computer Assisted Learning 19, no. 1 (February 25, 2003): 142–43. http://dx.doi.org/10.1046/j.1365-2729.2003.t01-1-00014.x.

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47

Celiešienė, Vilija. "Change in Word Meanings as One of the Features Signifying the Development of Computer Terms." Coactivity: Philology, Educology 24, no. 1 (June 7, 2016): 45–54. http://dx.doi.org/10.3846/cpe.2016.284.

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Анотація:

The article aims to analyse the change in meanings of standard language words viewed as a result of the development of computer terms. Words that, in the process of terminologisation and transterminologisation, have been transferred to computer terms from standard language or other subject fields and included in the Encyclopedic Dictionary of Computer Terms (Enciklopedinis kompiuterijos žodynas 2012) are analysed. The objects of the research were simple semantic formations and caiques, all of which were standard Lithuanian language words that either turned into terms in the field of computer terms or were transferred from other subject fields by inner borrowing. The research carried out has shown that most of the terms studied are semantic caiques formed – albeit merely outwardly – using own linguistic means yet having borrowed, most likely from English, their very concept and its definition. The Encyclopedic Dictionary of Computer Terms contains only a few single-word computer terms whose new meaning originated from the Lithuanian language and that could be regarded as semantic formations.

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48

Lucco, Steven. "Split-stream dictionary program compression." ACM SIGPLAN Notices 35, no. 5 (May 2000): 27–34. http://dx.doi.org/10.1145/358438.349307.

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49

Gao, Fangyuan, Xin Deng, Mai Xu, Jingyi Xu, and Pier Luigi Dragotti. "Multi-Modal Convolutional Dictionary Learning." IEEE Transactions on Image Processing 31 (2022): 1325–39. http://dx.doi.org/10.1109/tip.2022.3141251.

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50

Culpeper, Jonathan, and Phoebe Clapham. "The Borrowing of Classical and Romance Words into English." International Journal of Corpus Linguistics 1, no. 2 (January 1, 1996): 199–218. http://dx.doi.org/10.1075/ijcl.1.2.03cul.

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Анотація:

This paper describes a study designed to investigate the effect of Classical and Romance vocabulary on the English lexicon. The study involved a series of computer searches, using the CD-ROM version of the Oxford English Dictionary (1989). Our aim was to discover how many and at what point in time words were borrowed into English from Greek, Latin, French, Italian, Spanish, and Portuguese. The paper describes the general obstacles and limitations — both with the Oxford English Dictionary and the computer searches — that circumscribe our results. We then present our findings, relating them to extant research.

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