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Journal articles on the topic 'Tagging'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Mursyit, Mohammad, Aji Prasetya Wibawa, Ilham Ari Elbaith Zaeni, and Harits Ar Rosyid. "Pelabelan Kelas Kata Bahasa Jawa Menggunakan Hidden Markov Model." Mobile and Forensics 2, no. 2 (2020): 71–83. http://dx.doi.org/10.12928/mf.v2i2.2450.

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Part of Speech TaggingÂ atauÂ POS TaggingÂ adalah proses memberikan label pada setiap kata dalam sebuah kalimat secara otomatis. Penelitian ini menggunakan algoritmaÂ Hidden Markov ModelÂ (HMM) untuk prosesÂ POS Tagging. Perlakuan untukÂ unknown wordsÂ menggunakanÂ Most Probable POS-Tag.Â DatasetÂ yang digunakan berupa 10 cerita pendek berbahasa Jawa terdiri dari 10.180 kata yang telah diberikanÂ tagsetBahasa Jawa. Pada penelitian ini prosesÂ POS TaggingÂ menggunakan dua skenario. Skenario pertama yaitu menggunakan algoritmaÂ Hidden Markov ModelÂ (HMM) tanpa menggunakan perlakuan untukÂ unknow

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2

Rafferty, Pauline. "Tagging." KNOWLEDGE ORGANIZATION 45, no. 6 (2018): 500–516. http://dx.doi.org/10.5771/0943-7444-2018-6-500.

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3

Müller-Prove, Matthias. "Taxonomien und Folksonomien – Tagging als neues HCI-Element (Taxonomies and Folksonomies – Tagging as a New HCI Element)." i-com 6, no. 1 (2007): 14–18. http://dx.doi.org/10.1524/icom.2007.6.1.14.

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Community-Portale bieten immer häufiger kollaborative Tagging-Systeme zur zusätzlichen Auszeichnung ihrer Informationen an. Damit etabliert sich gerade ein neues Interface-Element, das das Potenzial zu einem erweiterten Umgang mit den Daten der gesamten digitalen Welt hat. Dieser Artikel beschäftigt sich mit den interaktiven Aspekten des Taggings, sowie mit der semantischen Ebene in Abgrenzung zum etablierten Wissensbegriff.

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4

Kang, Sin-Jae. "Learning Tagging Ontology from Large Tagging Data." Journal of Korean Institute of Intelligent Systems 18, no. 2 (2008): 157–62. http://dx.doi.org/10.5391/jkiis.2008.18.2.157.

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5

Hearn, William S., Kenneth H. Pollock, and Elizabeth N. Brooks. "Pre- and post-season tagging models: estimation of reporting rate and fishing and natural mortality rates." Canadian Journal of Fisheries and Aquatic Sciences 55, no. 1 (1998): 199–205. http://dx.doi.org/10.1139/f97-243.

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Brownie et al. (1985, U.S. Fish Wildl. Serv. Resour. Publ. 156, p. 159) presented models for tag returns from multiple taggings of animals when tagging is done twice per year. Here, we present a reformulation of their model suitable for pre- and post-season fishery tag return studies. Under this model, it is possible to estimate fishing mortality, natural mortality, and reporting rate from the tag return data alone. (Under once-a-year tagging models, the reporting rate usually has to be estimated externally.) We consider two special cases: (i) a pulse fishery and (ii) a continuous fishery over

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6

Bozzi, Nicola. "Tagging Aesthetics." A Peer-Reviewed Journal About 9, no. 1 (2020): 70–81. http://dx.doi.org/10.7146/aprja.v9i1.121490.

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   Social media have given social movements unprecedented tools for self-representation, however emancipatory identity politics are drowned out by the white noise of neoliberal self-branding practices. In response to this highly- aestheticised, de-politicised environment, we need a cultural re-negotiation of online categorisation. Rather than focusing on networks, this essay frames tagging as an everyday gesture of social media users that participates in the collective performance of identity. I argue this performance gives way to the materialisation of 'cultural avatars' – c

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7

Marr, James F. "Electronic tagging." Nursing Standard 4, no. 9 (1989): 54. http://dx.doi.org/10.7748/ns.4.9.54.s56.

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8

Brizzard, Bill. "Epitope tagging." BioTechniques 44, no. 5 (2008): 693–95. http://dx.doi.org/10.2144/000112841.

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9

Rubinstein, Daniel. "Tag, Tagging." Philosophy of Photography 1, no. 2 (2010): 197–200. http://dx.doi.org/10.1386/pop.1.2.197_7.

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10

Wang, Meng, Bingbing Ni, Xian-Sheng Hua, and Tat-Seng Chua. "Assistive tagging." ACM Computing Surveys 44, no. 4 (2012): 1–24. http://dx.doi.org/10.1145/2333112.2333120.

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11

Plehn, Tilman, and Michael Spannowsky. "Top tagging." Journal of Physics G: Nuclear and Particle Physics 39, no. 8 (2012): 083001. http://dx.doi.org/10.1088/0954-3899/39/8/083001.

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12

Wattenburg, B. "Fluorescent Tagging." Science 266, no. 5190 (1994): 1461. http://dx.doi.org/10.1126/science.266.5190.1461.

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13

EVERTS, SARAH. "TAGGING CHOLESTEROL." Chemical & Engineering News Archive 89, no. 4 (2011): 8. http://dx.doi.org/10.1021/cen-v089n004.p008a.

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14

Voas, Jeffrey, and Nir Kshetri. "Human Tagging." Computer 50, no. 10 (2017): 78–85. http://dx.doi.org/10.1109/mc.2017.3641646.

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15

LILLY, J. ROBERT. "Tagging Reviewed." Howard Journal of Criminal Justice 29, no. 4 (1990): 229–45. http://dx.doi.org/10.1111/j.1468-2311.1990.tb00685.x.

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16

d'Enfert, Christophe. "Pathogen tagging." Trends in Microbiology 6, no. 8 (1998): 304. http://dx.doi.org/10.1016/s0966-842x(98)01315-8.

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17

Zeilstra-Ryalls, Jill H., and Ronald L. Somerville. "Fragment tagging." Gene Analysis Techniques 7, no. 6 (1990): 151–59. http://dx.doi.org/10.1016/0735-0651(90)90029-f.

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18

Jarvik, Jonathan W., and Cheryl A. Telmer. "EPITOPE TAGGING." Annual Review of Genetics 32, no. 1 (1998): 601–18. http://dx.doi.org/10.1146/annurev.genet.32.1.601.

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19

Stacey, Tom. "Offender tagging." International Review of Law, Computers & Technology 4, no. 1 (1989): 197–200. http://dx.doi.org/10.1080/13600869.1989.9966288.

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20

Neff, Ellen P. "Tagging mitoribosomes." Lab Animal 49, no. 1 (2019): 19. http://dx.doi.org/10.1038/s41684-019-0457-9.

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21

Zaborowski, Betsy A. "Beyond tagging." ACM SIGACCESS Accessibility and Computing, no. 77-78 (September 2003): 1. http://dx.doi.org/10.1145/1029014.1028631.

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22

C.A.P. "Transposon tagging." Plant Molecular Biology Reporter 6, no. 1 (1988): 4–5. http://dx.doi.org/10.1007/bf02675299.

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23

Bethany Halford. "Tagging tryptophan." C&EN Global Enterprise 102, no. 9 (2024): 6. http://dx.doi.org/10.1021/cen-10209-scicon2.

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24

Wattenburg, Bill. "Fluorescent Tagging." Science 266, no. 5190 (1994): 1461. http://dx.doi.org/10.1126/science.266.5190.1461.a.

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25

Wang, Zhe, Abbas Nasiraei-Moghaddam, Meral L. Reyhan, Subashini Srinivasan, J. Paul Finn, and Daniel B. Ennis. "Complementary radial tagging for improved myocardial tagging contrast." Magnetic Resonance in Medicine 73, no. 4 (2014): 1432–40. http://dx.doi.org/10.1002/mrm.25259.

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26

Yamaguchi, Yuto, Mitsuo Yoshida, Christos Faloutsos, and Hiroyuki Kitagawa. "Patterns in Interactive Tagging Networks." Proceedings of the International AAAI Conference on Web and Social Media 9, no. 1 (2021): 513–22. http://dx.doi.org/10.1609/icwsm.v9i1.14616.

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How do users behave if they can tag each other in social networks?In this paper, we answer this question by studying the interactive tagging network constructed by Twitter lists. Twitter lists can be regarded as the tagging process; a user (i.e., tagger) creates a list with a name (i.e., tag) and adds other users (i.e., tagged users) into the list. This tagging network is by nature different from the resource tagging networks (e.g., Flickr and Delicious) because users on this network can tag each other. We address the following research questions: (RQ1) What is the common patterns and the diff

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27

Munk, Timme Bisgaard, and Kristian Mørk. "Folksonomies, Tagging Communities, and Tagging Strategies–An Empirical Study." KNOWLEDGE ORGANIZATION 34, no. 3 (2007): 115–27. http://dx.doi.org/10.5771/0943-7444-2007-3-115.

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28

Bergado Rosado, Jorge A. "Synaptic Tagging and Emotional tagging, linking Physiology and Psychology." International Journal of Psychophysiology 108 (October 2016): 40. http://dx.doi.org/10.1016/j.ijpsycho.2016.07.133.

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29

Ke, Xiayi, Marcos M. Miretti, John Broxholme, et al. "A comparison of tagging methods and their tagging space." Human Molecular Genetics 14, no. 18 (2005): 2757–67. http://dx.doi.org/10.1093/hmg/ddi309.

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30

Dhayalan, D., M. Queen Mary Vidya, and B. Gowri Priya. "Image Tagging With Social Assistance." International Journal of Trend in Scientific Research and Development Special Issue, Special Issue-Active Galaxy (2018): 43–47. http://dx.doi.org/10.31142/ijtsrd14566.

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31

Spiranec, Sonja, and Mislav Borovac. "Expert vs. Novices Dimensions of Tagging Behaviour in an Educational Setting." Bilgi Dünyası 13, no. 1 (2012): 1–16. http://dx.doi.org/10.15612/bd.2012.166.

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The organization and representation of information and knowledge have always been exclusively in the domain of professionals and experts. This has begun to change with the development of folksonomies as alternative, user-generated models of organizing information. The aim of this paper is to research the efficiency in tagging and folksonomy. The flexibility of tagging allows users to classify their collections of items in the ways that they find useful, but the personalized variety of terms can present challenges when searching and browsing. In order to determine the efficiency of tagging rese

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32

Zebari, Hawar M. H., Hoger M. Hidayet, Assel A. I. Al-nakshabandi, and Nizar Hussein. "Pain Caused by Ear Tagging in Kids of Native Black Goats." Journal of Scientific Research in Medical and Biological Sciences 2, no. 1 (2021): 19–29. http://dx.doi.org/10.47631/jsrmbs.v2i1.128.

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Purpose: The normal behavior of goat kids is influenced by painful husbandry procedures such as ear tagging, with kids reducing peripheral temperature and increase restlessness. The present study was designed to elucidate that pain caused by ear tagging affects peripheral temperatures and behavioral observations in Karadi kid goats. Study Design: Experimental Study Design. Subjects and Methods: Nineteen native black goat kids, aged 10-14 days, were used in this study as a sample. Eye and nasal temperatures were recorded before ear tagging process (control) and after ear tagging 5 times in 30 m

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33

Toelle, Sophia, Agnes Holtkamp, Uwe Blunck, Sebastian Paris, and Falk Schwendicke. "Improving the Bond Strength of Radiographically Tagged Caries Lesions In Vitro." Materials 13, no. 17 (2020): 3702. http://dx.doi.org/10.3390/ma13173702.

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After selective carious tissue removal, residual carious lesions remain radiographically detectable. Radiopaque tagging resolves the resulting diagnostic uncertainty but impedes bond strengths of adhesives to tagged dentin. We developed a protocol mitigating these detrimental effects. A 30%/50%/70% SnCl2 solution was dissolved in distilled water or a 30%/50%/90% ethanol solution (E30/60/90) and applied to artificially induced dentin lesions. Tagging effects were radiographically evaluated using transversal wavelength-independent microradiography (n = 6/group). Groups with sufficient tagging ef

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34

Frusher, S. D., S. D. Frusher, J. M. Hoenig, and J. M. Hoenig. "Strategies for improving the precision of fishing and natural mortality estimates from multiyear tagging models: a case study." Marine and Freshwater Research 52, no. 8 (2001): 1649. http://dx.doi.org/10.1071/mf01196.

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Fisheries scientists and managers want precise estimates of fishing and natural mortality for assessments and management decisions. Because tagging can be an expensive option, maximizing potential benefits requires careful consideration of experimental design. We evaluated four options for improving the precision of estimates by conducting Monte Carlo simulations of a fishery patterned after that for the rock lobsters in north-western Tasmania, Australia. Improving tag-reporting rate and increasing the duration of the tagging study provided greater improvements in precision than increasing eit

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35

Sak, Alexander. "Bayesian Belief Networks to handle NLP problems." E3S Web of Conferences 533 (2024): 03010. http://dx.doi.org/10.1051/e3sconf/202453303010.

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In corpus linguistics, part-of-speech tagging (POS tagging or PoS tagging or POST), also called grammatical tagging is the process of marking up a word in a text (corpus) as corresponding to a particular part of speech, based on both its definition and its context. Once performed by hand, POS tagging is now done in the context of computational linguistics, using algorithms which associate discrete terms, as well as hidden parts of speech, by a set of descriptive tags. POS-tagging algorithms fall into two distinctive groups: rule-based and stochastic. If rule –based algorithms are extremely com

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36

Xiao, Yongshun. "Growth Models with Corrections for the Retardative Effects of Tagging." Canadian Journal of Fisheries and Aquatic Sciences 51, no. 2 (1994): 263–67. http://dx.doi.org/10.1139/f94-027.

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Length increment data from mark–recapture experiments are commonly used to obtain information on animal growth, assuming that tagging does not affect the growth of marked animals. The assumption is violated in many studies, but the effects of tagging on growth and estimates of growth parameters have not been and cannot be examined without appropriate models. This paper describes a model allowing quantification and estimation of the retarding effects of tagging on animal growth simultaneously with growth parameters in all existing growth models, reduction or elimination of biases in growth para

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37

Strohmaier, Markus, Christian Körner, and Roman Kern. "Why do Users Tag? Detecting Users’ Motivation for Tagging in Social Tagging Systems." Proceedings of the International AAAI Conference on Web and Social Media 4, no. 1 (2010): 339–42. http://dx.doi.org/10.1609/icwsm.v4i1.14056.

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While recent progress has been achieved in understanding the structure and dynamics of social tagging systems, we know little about the underlying user motivations for tagging, and how they influence resulting folksonomies and tags. This paper addresses three issues related to this question: 1.) What motivates users to tag resources, and in what ways is user motivation amenable to quantitative analysis? 2.) Does users' motivation for tagging vary within and across social tagging systems, and if so how? and 3.) How does variability in user motivation influence resulting tags and folksonomies? I

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38

Zebaria, Hawar M. H., Hoger M. Hidayet, Assel A. I. Al-Naqshabendy, Nizar J. Hussein, and Nawroz A. Kakarash. "Pain Caused by Ear Tagging in Kids of Native Black Goats." Science Journal of University of Zakho 9, no. 1 (2021): 15–19. http://dx.doi.org/10.25271/sjuoz.2021.9.1.781.

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There is no study about changes in behaviour and peripheral temperature of kid goats undergoing ear tagging procedure. This study was designed to elucidate that pain caused by ear tagging affects peripheral temperatures and behavioural observations in Karadi kid goats. Nineteen native black goat kids, aged 10 – 14 days, were used in this study. Eye and nasal temperatures were recorded before the ear tagging process, and after ear tagging 5 periods in 30 minutes. Besides, the researchers observed each kid's behaviour for 30 minutes before ear tagging and 30 minutes after it, using focal samplin

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39

Muljono, Muljono, Umriya Afini, Catur Supriyanto, and Raden Arief Nugroho. "The Development of Indonesian POS Tagging System for Computer-aided Independent Language Learning." International Journal of Emerging Technologies in Learning (iJET) 12, no. 11 (2017): 138. http://dx.doi.org/10.3991/ijet.v12i11.7383.

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Word processing tool is a basic need in learning a language. One of the word processors needed by a language learner is part of speech (POS) tagging. While many POS Tagging tools for Indonesian language have been developed, no systems have been addressed specifically for language learners. This paper presents a study on an Indonesian part of speech (POS) tagging system developed as one of word processing tools for language learners. We use resources from previous Indonesian POS tagging research, such as MorphInd for the morphological analysis and IPOSTagger for part of speech tagging. Objectiv

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40

Hedden, Heather. "Tagging versus indexing." Indexer: The International Journal of Indexing 32, no. 2 (2014): 81–82. http://dx.doi.org/10.3828/indexer.2014.29.

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41

Dean, Jeffrey F. D. "Tagging all genes." Nature Biotechnology 22, no. 8 (2004): 961–62. http://dx.doi.org/10.1038/nbt0804-961.

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42

Monkman, Gareth. "Secure electronic tagging." Assembly Automation 20, no. 1 (2000): 24–27. http://dx.doi.org/10.1108/01445150010311626.

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43

Whalley, Katherine. "Emotional memory tagging." Nature Reviews Neuroscience 16, no. 3 (2015): 122. http://dx.doi.org/10.1038/nrn3927.

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44

Sebastiani, P., R. Lazarus, S. T. Weiss, L. M. Kunkel, I. S. Kohane, and M. F. Ramoni. "Minimal haplotype tagging." Proceedings of the National Academy of Sciences 100, no. 17 (2003): 9900–9905. http://dx.doi.org/10.1073/pnas.1633613100.

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45

Leslie, Mitch. "Tagging an organelle." Journal of Cell Biology 171, no. 2 (2005): 195. http://dx.doi.org/10.1083/jcb1712fta2.

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46

Steidel, Nora. "Tagging Human Rights." Zeitschrift für Menschenrechte 18, no. 2 (2024): 60–83. https://doi.org/10.46499/2417.3246.

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47

Ikonomidou, V. N., and G. D. Sergiadis. "Multirate spamm tagging." IEEE Transactions on Biomedical Engineering 50, no. 9 (2003): 1045–51. http://dx.doi.org/10.1109/tbme.2003.814524.

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48

Ede, Nicholas J., and Zemin Wu. "Beyond Rf tagging." Current Opinion in Chemical Biology 7, no. 3 (2003): 374–79. http://dx.doi.org/10.1016/s1367-5931(03)00061-9.

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49

Stern, P. R. "Tagging + Recruitment = Learning." Science Signaling 1, no. 8 (2008): ec75-ec75. http://dx.doi.org/10.1126/stke.18ec75.

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50

Borisov, G. "Combined b-tagging." Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 417, no. 2-3 (1998): 384–90. http://dx.doi.org/10.1016/s0168-9002(98)00777-3.

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