Relevant bibliographies by topics / Tagging

Academic literature on the topic 'Tagging'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 May 2024

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Journal articles on the topic "Tagging"

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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 (August 29, 2020): 71–83. http://dx.doi.org/10.12928/mf.v2i2.2450.

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Part of Speech Tagging atau POS Tagging adalah proses memberikan label pada setiap kata dalam sebuah kalimat secara otomatis. Penelitian ini menggunakan algoritma Hidden Markov Model (HMM) untuk proses POS Tagging. Perlakuan untuk unknown words menggunakan Most Probable POS-Tag. Dataset yang digunakan berupa 10 cerita pendek berbahasa Jawa terdiri dari 10.180 kata yang telah diberikan tagsetBahasa Jawa. Pada penelitian ini proses POS Tagging menggunakan dua skenario. Skenario pertama yaitu menggunakan algoritma Hidden Markov Model (HMM) tanpa menggunakan perlakuan untuk unknown words. Skenario yang kedua menggunakan HMM dan Most Probable POS-Tag untuk perlakuan unknown words. Hasil menunjukan skenario pertama menghasilkan akurasi sebesar 45.5% dan skenario kedua menghasilkan akurasi sebesar 70.78%. Most Probable POS-Tag dapat meningkatkan akurasi pada POS Tagging tetapi tidak selalu menunjukan hasil yang benar dalam pemberian label. Most Probable POS-Tag dapat menghilangkan probabilitas bernilai Nol dari POS Tagging Hidden Markov Model. Hasil penelitian ini menunjukan bahwa POS Tagging dengan menggunakan Hidden Markov Model dipengaruhi oleh perlakuan terhadap unknown words, perbendaharaan kata dan hubungan label kata pada dataset.  Part of Speech Tagging or POS Tagging is the process of automatically giving labels to each word in a sentence. This study uses the Hidden Markov Model (HMM) algorithm for the POS Tagging process. Treatment for unknown words uses the Most Probable POS-Tag. The dataset used is in the form of 10 short stories in Javanese consisting of 10,180 words which have been given the Javanese tagset. In this study, the POS Tagging process uses two scenarios. The first scenario is using the Hidden Markov Model (HMM) algorithm without using treatment for unknown words. The second scenario uses HMM and Most Probable POS-Tag for treatment of unknown words. The results show that the first scenario produces an accuracy of 45.5% and the second scenario produces an accuracy of 70.78%. Most Probable POS-Tag can improve accuracy in POS Tagging but does not always produce correct labels. Most Probable POS-Tag can remove zero-value probability from POS Tagging Hidden Markov Model. The results of this study indicate that POS Tagging using the Hidden Markov Model is influenced by the treatment of unknown words, vocabulary and word label relationships in the dataset.
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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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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 (May 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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Kang, Sin-Jae. "Learning Tagging Ontology from Large Tagging Data." Journal of Korean Institute of Intelligent Systems 18, no. 2 (April 25, 2008): 157–62. http://dx.doi.org/10.5391/jkiis.2008.18.2.157.

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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 (January 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 part of the year. An artificial example and simulation results are presented to illustrate the methodology and the properties of the various estimators. Unlike for catch-based methods, the correlation between estimates of fishing mortality and natural mortality is moderate. While pre- and post-season tagging studies are likely to be difficult to run in practice, other methods of estimating reporting rate are also difficult to implement, and therefore, this approach may prove quite useful, especially in fisheries that have heavy exploitation rates.
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Bozzi, Nicola. "Tagging Aesthetics." A Peer-Reviewed Journal About 9, no. 1 (August 4, 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' – collective identity figures that lie beyond coherent representation and can reinforce reductive social stereotypes or inspire politically critical figurations. Apart from offering a cultural critique of tagging itself, the essay discusses a range of creative approaches to tagging that de-naturalise processes of online categorisation by drawing critical attention towards them.
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Marr, James F. "Electronic tagging." Nursing Standard 4, no. 9 (November 22, 1989): 54. http://dx.doi.org/10.7748/ns.4.9.54.s56.

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

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

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

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Dissertations / Theses on the topic "Tagging"

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Söderlund, Joakim. "Universal Tagging." Thesis, Linnéuniversitetet, Institutionen för datavetenskap (DV), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-37075.

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The goal of this master degree project was to find out whether or not a highly integrated system for universal tagging of content improves the usability of a desktop environment. A prototype of such a system was implemented and integrated into the GNOME desktop environment. A usability study was then performed which showed that the tagging system did improve the usability of the desktop environment.
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Leslie, James. "Collaborative Genre Tagging." Master's thesis, Faculty of Science, 2020. http://hdl.handle.net/11427/32402.

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Recommender systems (RS) are used extensively in online retail and on media streaming platforms to help users filter the plethora of options at their disposal. Their goal is to provide users with suggestions of products or artworks that they might like. Content-based RS's make use of user and/or item metadata to predict user preferences, while collaborative-filtering (CF) has proven to be an effective approach in tasks such as predicting movie or music preferences of users in the absence of any metadata. Latent factor models have been used to achieve state-of-the-art accuracy in many CF settings, playing an especially large role in beating the benchmark set in the Netflix Prize in 2008. These models learn latent features for users and items to predict the preferences of users. The first latent factor models made use of matrix factorisation to learn latent factors, but more recent approaches have made use of neural architectures with embedding layers. This master's dissertation outlines collaborative genre tagging (CGT), a transfer learning application of CF that makes use of latent factors to predict genres of movies, using only explicit user ratings as model inputs.
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Lewis, Stacey. "VideoTag : encouraging the effective tagging of internet videos through tagging games." Thesis, University of Wolverhampton, 2014. http://hdl.handle.net/2436/621745.

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The tags and descriptions entered by video owners in video sharing sites are typically inadequate for retrieval purposes, yet the majority of video search still uses this text. This problem is escalating due to the ease with which users can self-publish videos, generating masses that are poorly labelled and poorly described. This thesis investigates how users tag videos and whether video tagging games can solve this problem by generating useful sets of tags. A preliminary study investigated tags in two social video sharing sites, YouTube and Viddler. YouTube contained many irrelevant tags because the system does not encourage users to tag their videos and does not promote tags as useful. In contrast, using tags as the sole means of categorisation in Viddler motivated users to enter a higher proportion of relevant tags. Poor tags were found in both systems, however, highlighting the need to improve video tagging. In order to give users incentives to tag videos, the VideoTag project in this thesis developed two tagging games, Golden Tag and Top Tag, and one non-game tagging system, Simply Tag, and conducted two experiments with them. In the first experiment VideoTag was a portal to play video tagging games whereas in the second experiment it was a portal to curate collections of special interest videos. Users preferred to tag videos using games, generating tags that were relevant to the videos and that covered a range of tag types that were descriptive of the video content at a predominately specific, objective level. Users were motivated by interest in the content rather than by game elements, and content had an effect on the tag types used. In each experiment, users predominately tagged videos using objective language, with a tendency to use specific rather than basic tags. There was a significant difference between the types of tags entered in the games and in Simply Tag, with more basic, objective vocabulary entered into the games and more specific, objective language entered into the non-game system. Subjective tags were rare but were more frequent in Simply Tag. Gameplay also had an influence on the types of tags entered; Top Tag generated more basic tags and Golden Tag generated more specific and subjective tags. Users were not attracted to use VideoTag by the games alone. Game mechanics had little impact on motivations to use the system. VideoTag used YouTube videos, but could not upload the tags to YouTube and so users could see no benefit for the tags they entered, reducing participation. Specific interest content was more of a motivator for use than games or tagging and that this warrants further research. In the current game-saturated climate, gamification of a video tagging system may therefore be most successful for collections of videos that already have a committed user base.
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Caryl, A. P. "Gene tagging in Arabidopsis." Thesis, University of Cambridge, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.597347.

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The work described in this thesis takes two approaches towards the identification of tagged genes in Arabidopsis thaliana. A family screening approach was used to isolate mutants from the T-DNA transformed Feldmann lines of Arabidopsis. Co-segregation analysis was used to determine whether these mutants were tagged. None of the mutants isolated showed co-segregation with the T-DNA so this approach was abandoned. Enhancer trap constructs are designed to report the activity of enhancers near a transgene insert. Typically they contain a reporter gene with an weak promoter in a transformation vector. The second approach used in this study was to investigate a collection of 123 independently transformed lines of Arabidopsis containing such constructs which were available in the laboratory. The construct introduced into these lines contained a β-Glucuronidase reporter gene under the control of an attenuated Cauliflower Mosaic Virus promoter. The number of independent active inserts, T-DNA copy number and GUS staining patterns of the lines were investigated. One of the lines, Δ31, showed GUS staining associated with meristematic tissue (with the exception of the primary root meristem). The expression of the GUS reporter gene was presumably being controlled by a plant enhancer adjacent to the insert. Presumably the enhancer would normally act to regulate the expression of an endongenous plant gene. Spectrophotometric assays were used to measure the response to auxin in root tissues. IPCR was used to amplify plant DNA flanking the insert in line Δ31. A clone of this DNA was used to isolate four large overlapping clones from a lambda genomic library of wild-type plants which would be likely to contain the tagged enhancer, and any endogenous gene regulated by this enhancer.
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Neto, Hélia Paula da Silva. "Activation tagging in petunia." Thesis, University of Leeds, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.418781.

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Gerber, Michael. "Visualisierung von 3D Tagging Datensätzen." Zürich : ETH, Eidgenössische Technische Hochschule Zürich, Institut für Biomedizinische Technik, 2002. http://e-collection.ethbib.ethz.ch/show?type=dipl&nr=53.

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Loftsson, Hrafn. "Tagging and parsing Icelandic text." Thesis, University of Sheffield, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.487602.

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~aturallanguageprocessing (~LP) is a very young discipline in Iceland. Therefore, there is a lack of publicly available basic tools for processing the morphologically complex Icelandic language. III this thesis, we investigate the effectiveness and viability of using (mainly) rule-based methods for analysing the synta.x of Icelandic text. For this purpose, and because our work has a practical focus, we develop a ~LP toolkit, IceNLP. The toolkit consists of a tokeniser, the morphological analyser IceMorphy, the part-ofspeech tagger IceTagger', and the shallow parser IcePan;er'. The task of the tokeniser is to split a sequence of characters into linguistic units and identify where one sentence ends and another one begins. IceMorphy is used for guessing part-of-speech tags for unknown words and filling in tag profile gaps ill a dictionary. Ice Tagger' is a linguistic rule-based tagger which achieves considerably higher tagging accuracy than previously reported results using taggers based on datadriven techniques. Furthermore, by using several tagger integration and combination methods. we increase substantially the tagging accuracy of Icelandic text, with regard to previous work. Our shallow parser, IceParser, is an incremental finite-state parser, the first parser puulished for the Icelandic language. It produces shallow syntactic annotation, using an annotation scheme specifically developed in this work. Furthermore, we create a grammar definition corpus, a representative collection of sentences annotated using the annotation scheme. The development of our toolkit is a step towards the goal of building a Basic Language Resource Kit (BLARK) for the Icelandic language. Our toolkit has been made available for use in the research community, and should therefore encourage further research and development of XLP tools.
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Bishop, Gerard James. "Transposon tagging in Lycopersicon esculentum." Thesis, University of East Anglia, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.297010.

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Alharbi, Ghada. "Metadiscourse tagging in academic lectures." Thesis, University of Sheffield, 2016. http://etheses.whiterose.ac.uk/16475/.

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This thesis presents a study into the nature and structure of academic lectures, with a special focus on metadiscourse phenomena. Metadiscourse refers to a set of linguistics expressions that signal specific discourse functions such as the Introduction: “Today we will talk about ... ” and Emphasising: “This is an important point”. These functions are important because they are part of lecturers’ strategies in understanding of what happens in a lecture. The knowledge of their presence and identity could serve as initial steps toward downstream applications that will require functional analysis of lecture content such as a browser for lectures archives, summarisation, or an automatic minute-taker for lectures. One challenging aspect for metadiscourse detection and classification is that the set of expressions are semi-fixed, meaning that different phrases can indicate the same function. To that end a four-stage approach is developed to study metadiscourse in academic lectures. Firstly, a corpus of metadiscourse for academic lectures from Physics and Economics courses is built by adapting an existing scheme that describes functional-oriented metadiscourse categories. Second, because producing reference transcripts is a time-consuming task and prone to some errors due to the manual efforts required, an automatic speech recognition (ASR) system is built specifically to produce transcripts of lectures. Since the reference transcripts lack time-stamp information, an alignment system is applied to the reference to be able to evaluate the ASR system. Then, a model is developed using Support Vector Machines (SVMs) to classify metadiscourse tags using both textual and acoustical features. The results show that n-grams are the most inductive features for the task; however, due to data sparsity the model does not generalise for unseen n-grams. This limits its ability to solve the variation issue in metadiscourse expressions. Continuous Bag-of-Words (CBOW) provide a promising solution as this can capture both the syntactic and semantic similarities between words and thus is able to solve the generalisation issue. However, CBOW ignores the word order completely, something which is very important to be retained when classifying metadiscourse tags. The final stage aims to address the issue of sequence modelling by developing a joint CBOW and Convolutional Neural Network (CNN) model. CNNs can work with continuous features such as word embedding in an elegant and robust fashion by producing a fixed-size feature vector that is able to identify indicative local information for the tagging task. The results show that metadiscourse tagging using CNNs outperforms the SVMs model significantly even on ASR outputs, owing to its ability to predict a sequence of words that is more representative for the task regardless of its position in the sentence. In addition, the inclusion of other features such as part-of-speech (POS) tags and prosodic cues improved the results further. These findings are consistent in both disciplines. The final contribution in this thesis is to investigate the suitability of using metadiscourse tags as discourse features in the lecture structure segmentation model, despite the fact that the task is approached as a classification model and most of the state-of-art models are unsupervised. In general, the obtained results show remarkable improvements over the state-of-the-art models in both disciplines.
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Togia, Theodosia. "The language of collaborative tagging." Thesis, University of Cambridge, 2015. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.709451.

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Books on the topic "Tagging"

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Heinlein, Manfred, ed. RNA Tagging. New York, NY: Springer US, 2020. http://dx.doi.org/10.1007/978-1-0716-0712-1.

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Martha, Cooper. Name tagging. New York: Mark Batty Pub., 2010.

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van Halteren, Hans, ed. Syntactic Wordclass Tagging. Dordrecht: Springer Netherlands, 1999. http://dx.doi.org/10.1007/978-94-015-9273-4.

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Washington (State). Dept. of Ecology., ed. Well tagging requirements. [Olympia, Wash.]: Washington State Dept. of Ecology, 1998.

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Halteren, Hans. Syntactic Wordclass Tagging. Dordrecht: Springer Netherlands, 1999.

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Ody, Penelope. Tagging the future. Drapers Record: 22 November, 1995.

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Sajikumar, Sreedharan, ed. Synaptic Tagging and Capture. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4939-1761-7.

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Sajikumar, Sreedharan, and Ted Abel, eds. Synaptic Tagging and Capture. Cham: Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-54864-2.

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Schlueter, Lynn R. Lake Ashtabula walleye tagging project. Bismarck, N.D: North Dakota Game and Fish Dept., 2004.

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Bengtsson, Camilla. Segmenting and tagging parallel corpora. Uppsala: Uppsala University Department of Linguistics, 2000.

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Book chapters on the topic "Tagging"

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Weik, Martin H. "tagging." In Computer Science and Communications Dictionary, 1732. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_19023.

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Uther, William, Dunja Mladenić, Massimiliano Ciaramita, Bettina Berendt, Aleksander Kołcz, Marko Grobelnik, Dunja Mladenić, et al. "Tagging." In Encyclopedia of Machine Learning, 955. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_810.

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Roland, Michael. "Tagging." In Security Issues in Mobile NFC Devices, 69–101. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15488-6_5.

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Rennie, Frank, and Keith Smyth. "Tagging." In Digital Learning: The Key Concepts, 140–41. 2nd ed. London: Routledge, 2019. http://dx.doi.org/10.4324/9780429425240-190.

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Breslin, John G., Alexandre Passant, and Stefan Decker. "Social tagging." In The Social Semantic Web, 137–58. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01172-6_8.

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Zhu, Xiaofeng. "Multimedia Tagging." In Encyclopedia of Database Systems, 1–4. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4899-7993-3_80632-1.

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Weik, Martin H. "aperture tagging." In Computer Science and Communications Dictionary, 57. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_749.

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Zhang, Xinhua, Novi Quadrianto, Kristian Kersting, Zhao Xu, Yaakov Engel, Claude Sammut, Mark Reid, et al. "Grammatical Tagging." In Encyclopedia of Machine Learning, 459. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_347.

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Zeugmann, Thomas, Pascal Poupart, James Kennedy, Xin Jin, Jiawei Han, Lorenza Saitta, Michele Sebag, et al. "POS Tagging." In Encyclopedia of Machine Learning, 776–79. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_643.

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Shekhar, Shashi, and Hui Xiong. "Radio Tagging." In Encyclopedia of GIS, 949. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_1072.

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Conference papers on the topic "Tagging"

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Abrouk, Lylia, David Gross-Amblard, Nadine Cullot, and Virginie Thion-Goasdoue. "Tagging Resources, Tagging Communities." In 2010 Seventh International Conference on Information Technology: New Generations. IEEE, 2010. http://dx.doi.org/10.1109/itng.2010.206.

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Werner, Claudia, Mario Pimenta, and Joao Varela. "Fado tagging system: a new tagging approach." In Computing for high luminosity and high intensity facilities. AIP, 1990. http://dx.doi.org/10.1063/1.39527.

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Krause, Markus, and Hidir Aras. "Playful tagging." In the 18th international conference. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1526709.1526931.

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Shen, Jialie, Meng Wang, Shuicheng Yan, and Xian-Sheng Hua. "Multimedia tagging." In the 19th ACM international conference. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/2072298.2072405.

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Strohmaier, Markus. "Purpose tagging." In Proceeding of the 2008 ACM workshop. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1458583.1458603.

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Holstrom, Chris. "Social Tagging." In JCDL '18: The 18th ACM/IEEE Joint Conference on Digital Libraries. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3197026.3197065.

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Yang, Kuiyuan, Xian-Sheng Hua, Meng Wang, and Hong-Jiang Zhang. "Tagging tags." In the international conference. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1873951.1874035.

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Zaborowski, Betsy A. "Beyond tagging." In the ACM SIGACCESS conference. New York, New York, USA: ACM Press, 2004. http://dx.doi.org/10.1145/1028630.1028631.

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Park, Yongtae, and Hyogon Kim. "Tagging cities?" In the 10th ACM Conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2426656.2426693.

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Sánchez, J. Alfredo, Adriana Arzamendi-Pétriz, and Omar Valdiviezo. "Induced tagging." In the 2007 conference. New York, New York, USA: ACM Press, 2007. http://dx.doi.org/10.1145/1255175.1255252.

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Reports on the topic "Tagging"

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Bock, Geoffrey. Tagging Content for Meaning. Boston, MA: Patricia Seybold Group, December 2004. http://dx.doi.org/10.1571/psgp12-9-04cc.

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Corscadden, Louise, and Arpaporn Sutipatanasomboon. Rodent Tagging And Identification. ConductScience, January 2023. http://dx.doi.org/10.55157/cs20230109.

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Animal tagging is a means to identify and distinguish all the individual animals of interest, which applies to wildlife, farm, or laboratory animals. It involves attaching a tag to a specific animal part that contains a unique identifier for each animal. The identifier can be numbers, alphabets, or a combination of both that distinguish and track the animals throughout their lifespans. In rodents, tagging is the most popular identification approach. Typically, tags are made from metals and attached to the outer part of rodent ears, or the ear pinna. In rare circumstances, metal tags can also be attached to the rodent’s leg or tail.
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Whistler, K., and G. Adams. Language Tagging in Unicode Plain Text. RFC Editor, January 1999. http://dx.doi.org/10.17487/rfc2482.

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4

Michelmore, R. W. Transposon tagging of disease resistance genes. Office of Scientific and Technical Information (OSTI), January 1989. http://dx.doi.org/10.2172/6038413.

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Ma, Huanfeng, Burcu Karagol-Ayan, David Doermann, Doug Oard, and Jianqiang Wang. Parsing and Tagging of Bilingual Dictionary. Fort Belvoir, VA: Defense Technical Information Center, September 2003. http://dx.doi.org/10.21236/ada459226.

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Bloom, Rose. Photoluminescence in Tagging: A Glowing Review. Office of Scientific and Technical Information (OSTI), November 2021. http://dx.doi.org/10.2172/1833235.

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Pizarro, Rodrigo, Raúl Delgado, Huáscar Eguino, and Aloisio Lopes Pereira. Climate Change Public Budget Tagging: Connections across Financial and Environmental Classification Systems. Inter-American Development Bank, January 2021. http://dx.doi.org/10.18235/0003021.

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Abstract:
Identifying and evaluating climate expenditures in the public sector, known as budget tagging, has generated increasing attention from multiple stakeholders, not only to assess the governments climate change policy, but also to monitor fiscal risks associated with increasing and unpredictable climate change impacts. This paper explores the issues raised by climate change budget tagging in the context of a broader discussion on the connections with fiscal and environmental statistical classification systems. It argues that, for climate change budget tagging efforts to be successful, the definitions and classifications of climate change expenditures must be consistent with statistical standards currently in use, such as the Government Finance Statistics Framework and the System of National Accounts.
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Wicklund, A. B., and K. Byrum. Self tagging studies using semileptonic B decays. Office of Scientific and Technical Information (OSTI), March 1994. http://dx.doi.org/10.2172/10141166.

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Ackermann, Mark R., Paul A. Cahill, Timothy J. Drummond, and Jess Patrick Wilcoxon. A brief examination of optical tagging technologies. Office of Scientific and Technical Information (OSTI), July 2003. http://dx.doi.org/10.2172/917128.

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Hook, Anson, Martin Jankowiak, and Jay G. Wacker. Jet Dipolarity: Top Tagging with Color Flow. Office of Scientific and Technical Information (OSTI), August 2011. http://dx.doi.org/10.2172/1022564.

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