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1
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, HeÌlia 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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Rahuma, Awatef. "Semantically-enhanced image tagging system." Thesis, De Montfort University, 2013. http://hdl.handle.net/2086/9494.
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In multimedia databases, data are images, audio, video, texts, etc. Research interests in these types of databases have increased in the last decade or so, especially with the advent of the Internet and Semantic Web. Fundamental research issues vary from unified data modelling, retrieval of data items and dynamic nature of updates. The thesis builds on findings in Semantic Web and retrieval techniques and explores novel tagging methods for identifying data items. Tagging systems have become popular which enable the users to add tags to Internet resources such as images, video and audio to make them more manageable. Collaborative tagging is concerned with the relationship between people and resources. Most of these resources have metadata in machine processable format and enable users to use free- text keywords (so-called tags) as search techniques. This research references some tagging systems, e.g. Flicker, delicious and myweb2.0. The limitation with such techniques includes polysemy (one word and different meaning), synonymy (different words and one meaning), different lexical forms (singular, plural, and conjugated words) and misspelling errors or alternate spellings. The work presented in this thesis introduces semantic characterization of web resources that describes the structure and organization of tagging, aiming to extend the existing Multimedia Query using similarity measures to cater for collaborative tagging. In addition, we discuss the semantic difficulties of tagging systems, suggesting improvements in their accuracies. The scope of our work is classified as follows: (i) Increase the accuracy and confidence of multimedia tagging systems. (ii) Increase the similarity measures of images by integrating varieties of measures. To address the first shortcoming, we use the WordNet based on a tagging system for social sharing and retrieval of images as a semantic lingual ontology resource. For the second shortcoming we use the similarity measures in different ways to recognise the multimedia tagging system. Fundamental to our work is the novel information model that we have constructed for our computation. This is based on the fact that an image is a rich object that can be characterised and formulated in n-dimensions, each dimension contains valuable information that will help in increasing the accuracy of the search. For example an image of a tree in a forest contains more information than an image of the same tree but in a different environment. In this thesis we characterise a data item (an image) by a primary description, followed by n-secondary descriptions. As n increases, the accuracy of the search improves. We give various techniques to analyse data and its associated query. To increase the accuracy of the tagging system we have performed different experiments on many images using similarity measures and various techniques from VoI (Value of Information). The findings have shown the linkage/integration between similarity measures and that VoI improves searches and helps/guides a tagger in choosing the most adequate of tags.
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McGinley, Susan. "Tagging Fish: Monitoring Native Species." College of Agriculture and Life Sciences, University of Arizona (Tucson, AZ), 2000. http://hdl.handle.net/10150/622273.
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Gustafsson, Emanuel. "Automatic Tagging of Salary Differences." Thesis, Örebro universitet, Institutionen för naturvetenskap och teknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-92187.
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Jämlikhet på arbetsplatser är fortfarande ett stort problem särskilt när det kommer till lönesättning. För att hjälpa till att lösa det problemet har Sysarb utvecklat olika verktyg för att hitta icke jämlika förhållanden i löner på företag och organisationer. Ett av de verktygen är ett löneanalysverktyg som hjälper till att jämföra löner så de är jämlika med avseende på kön, ålder och andra faktorer. Vid lönanalyser är en av de viktigaste delarna att förklara skillnaderna i lön mellan olika grupper av arbetare. Denna process sker just nu manuellt av den chef som leder arbetet genom att välja en av fem förinställda taggar. För att snabba upp processen och göra den mindre repetitiv för chefer utforskar den här rapporten möjligheten att automatisera processen med maskinlärning. För att uppfylla det här målet var algoritmerna boosted decision tree, random forest decision tree och logistic regression utvärderade för att hitta den som bäst löste problemet. För att träna modellerna så användes data insamlad från Sysarbs löneanalysverktyg.Workplace equality is still a big problem especially when it comes to salaries. To help solve this problem Sysarb has developed software to help find the inequalities in salaries. One of the tools they have developed is a salary analysis tool for comparing the salaries in a company or organization to make sure it is fair across gender, age and other factors. When making a salary analysis one of the most important parts is to explain the difference in salary between different groups of workers. This process is currently done manually by the managers responsible by selecting one of five premade tags. To speed up the process and make it less tedious for the managers this report aims to explore the possibility of automating the process using machine learning. To achieve this goal the algorithms boosted decision tree, random forest decision tree and logistic regression were evaluated to find which one best solved the problem. For training the models real world data collected from Sysarbs salary software was used.Keywords:
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Hoyt, Matthew Ray. "Automatic Tagging of Communication Data." Thesis, University of North Texas, 2012. https://digital.library.unt.edu/ark:/67531/metadc149611/.
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Globally distributed software teams are widespread throughout industry. But finding reliable methods that can properly assess a team's activities is a real challenge. Methods such as surveys and manual coding of activities are too time consuming and are often unreliable. Recent advances in information retrieval and linguistics, however, suggest that automated and/or semi-automated text classification algorithms could be an effective way of finding differences in the communication patterns among individuals and groups. Communication among group members is frequent and generates a significant amount of data. Thus having a web-based tool that can automatically analyze the communication patterns among global software teams could lead to a better understanding of group performance. The goal of this thesis, therefore, is to compare automatic and semi-automatic measures of communication and evaluate their effectiveness in classifying different types of group activities that occur within a global software development project. In order to achieve this goal, we developed a web-based component that can be used to help clean and classify communication activities. The component was then used to compare different automated text classification techniques on various group activities to determine their effectiveness in correctly classifying data from a global software development team project.
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DESSI, ANDREA. "Toward Automatic RDF Property Tagging." Doctoral thesis, Università degli Studi di Cagliari, 2017. http://hdl.handle.net/11584/249538.
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This work investigates some problems about semantic properties (also
known as predicates) of Knowledge Bases, as part of the Semantic Web,
for querying and ranking them toward a new system to tag automatically
RDF Property over parts of free-text in Natural Language.
The main insights and contributions are:
- a contribution to develop a system called Qpedia, inspired by SWiPE,
to make difficult query on schema-agnostic Knowledge Bases with a
simple and intuitive mobile-user interface;
- the creation of the first approach exploiting Machine-Learning to
rank RDF predicates;
- the creation of a possible approach to tagging free-text with RDF
predicates, with a case study of possible backend;
The proposed methods have been evaluated with the most popular Knowledge Bases
(DBpedia, WikiData, MusicBrainz and Freebase), obtaining
encouraging results. Thus, this work is a first step towards the RDF
Property Tagging of natural language, as reflected in Chapter 5, needed
to pave the way providing a resolution of sub-problems related to Question Answering
over RDF properties, which are not typically addressed in literature through this way.This work investigates some problems about semantic properties (also known as predicates) of Knowledge Bases, as part of the Semantic Web, for querying and ranking them toward a new system to tag automatically RDF Property over parts of free-text in Natural Language. The main insights and contributions are: - a contribution to develop a system called Qpedia, inspired by SWiPE, to make difficult query on schema-agnostic Knowledge Bases with a simple and intuitive mobile-user interface; - the creation of the first approach exploiting Machine-Learning to rank RDF predicates; - the creation of a possible approach to tagging free-text with RDF predicates, with a case study of possible backend; The proposed methods have been evaluated with the most popular Knowledge Bases (DBpedia, WikiData, MusicBrainz and Freebase), obtaining encouraging results. Thus, this work is a first step towards the RDF Property Tagging of natural language, as reflected in Chapter 5, needed to pave the way providing a resolution of sub-problems related to Question Answering over RDF properties, which are not typically addressed in literature through this way.
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Ruppert, Jörg. "Epitope-Tagging des humanen TSH-Rezeptors." [S.l.] : [s.n.], 2000. http://www.sub.uni-hamburg.de/disse/223/Disse.pdf.
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Neiman, Mårten. "Tagging systems for sequencing large cohorts." Licentiate thesis, KTH, KTH, Science for Life Laboratory, SciLifeLab, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-24365.
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Advances in sequencing technologies constantly improves the throughput andaccuracy of sequencing instruments. Together with this development comes newdemands and opportunities to fully take advantage of the massive amounts of dataproduced within a sequence run. One way of doing this is by analyzing a large set ofsamples in parallel by pooling them together prior to sequencing and associating thereads to the corresponding samples using DNA sequence tags. Amplicon sequencingis a common application for this technique, enabling ultra deep sequencing andidentification of rare allelic variants. However, a common problem for ampliconsequencing projects is formation of unspecific PCR products and primer dimersoccupying large portions of the data sets.
This thesis is based on two papers exploring these new kinds of possibilities andissues. In the first paper, a method for including thousands of samples in the samesequencing run without dramatically increasing the cost or sample handlingcomplexity is presented. The second paper presents how the amount of high qualitydata from an amplicon sequencing run can be maximized.
The findings from the first paper shows that a two-tagging system, where the first tagis introduced by PCR and the second tag is introduced by ligation, can be used foreffectively sequence a cohort of 3500 samples using the 454 GS FLX Titaniumchemistry. The tagging procedure allows for simple and easy scalable samplehandling during sequence library preparation. The first PCR introduced tags, that arepresent in both ends of the fragments, enables detection of chimeric formation andhence, avoiding false typing in the data set.
In the second paper, a FACS-machine is used to sort and enrich target DNA covered emPCR beads. This is facilitated by tagging quality beads using hybridization of afluorescently labeled target specific DNA probe prior to sorting. The system wasevaluated by sequencing two amplicon libraries, one FACS sorted and one standardenriched, on the 454 showing a three-fold increase of quality data obtained.
QC20100907
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Awg, Iskandar Dayang Nurfatimah, and dnfaiz@fit unimas my. "Image Retrieval using Automatic Region Tagging." RMIT University. Computer Science and Information Technology, 2008. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20090302.155704.
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The task of tagging, annotating or labelling image content automatically with semantic keywords is a challenging problem. To automatically tag images semantically based on the objects that they contain is essential for image retrieval. In addressing these problems, we explore the techniques developed to combine textual description of images with visual features, automatic region tagging and region-based ontology image retrieval. To evaluate the techniques, we use three corpora comprising: Lonely Planet travel guide articles with images, Wikipedia articles with images and Goats comic strips. In searching for similar images or textual information specified in a query, we explore the unification of textual descriptions and visual features (such as colour and texture) of the images. We compare the effectiveness of using different retrieval similarity measures for the textual component. We also analyse the effectiveness of different visual features extracted from the images. We then investigate the best weight combination of using textual and visual features. Using the queries from the Multimedia Track of INEX 2005 and 2006, we found that the best weight combination significantly improves the effectiveness of the retrieval system. Our findings suggest that image regions are better in capturing the semantics, since we can identify specific regions of interest in an image. In this context, we develop a technique to tag image regions with high-level semantics. This is done by combining several shape feature descriptors and colour, using an equal-weight linear combination. We experimentally compare this technique with more complex machine-learning algorithms, and show that the equal-weight linear combination of shape features is simpler and at least as effective as using a machine learning algorithm. We focus on the synergy between ontology and image annotations with the aim of reducing the gap between image features and high-level semantics. Ontologies ease information retrieval. They are used to mine, interpret, and organise knowledge. An ontology may be seen as a knowledge base that can be used to improve the image retrieval process, and conversely keywords obtained from automatic tagging of image regions may be useful for creating an ontology. We engineer an ontology that surrogates concepts derived from image feature descriptors. We test the usability of the constructed ontology by querying the ontology via the Visual Ontology Query Interface, which has a formally specified grammar known as the Visual Ontology Query Language. We show that synergy between ontology and image annotations is possible and this method can reduce the gap between image features and high-level semantics by providing the relationships between objects in the image. In this thesis, we conclude that suitable techniques for image retrieval include fusing text accompanying the images with visual features, automatic region tagging and using an ontology to enrich the semantic meaning of the tagged image regions.
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Rostron, Daniel C. "Spectroscopy of 250Fm using Tagging Techniques." Thesis, University of Liverpool, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.510972.
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Jalaly, Imad. "RF barcodes for identification and tagging." Thesis, University of Surrey, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.593894.
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A novel idea is investigated for radio frequency identification (RFID) and tagging, based on a radio frequency analogy of the ubiquitous optical barcode. The proposed method, referred to as an RF barcode, is based upon microwave resonators of different frequencies and requires no integrated circuits. RF barcodes, like optical barcodes, are passive devices requiring neither batteries nor reader power. These low cost components can potentially be "printed" on the tagged items or the packaging using metal-loaded ink. Each tag consists of parallel strips of microstrip half wavelength dipoles with varying widths as well varying lengths, each with a specific resonant frequency. A collection of these resonant dipoles forms a multi-bit tag with a specific binary code. With n different resonant RF barcodes, 2n_1 different items can be tagged and identified. Like optical barcodes, RF barcodes are disposable and read-only devices since the barcode reader cannot alter the information they contain, hence no readwrite capability as,is found with chip-based RFID devices. This is often referred to as a write-once, read-many (WORM) tag type_ It is shown that RF barcodes have longer read range compared to optical barcodes; a range of up to 10m is achievable using allowable transmit levels in the license-exempt Industrial Scientific and Medical (ISM) frequency bands. It is shown for the first time that a useable number of bits can be achieved by operating within multiple bands. Prototype tags are demonstrated in the 2.4 GHz and 5-6 GHz frequency bands. Techniques are presented for accurately estimating barcode centre frequency, quality factor and bandwidth in order to maximise the number of information bits in the tag. A number of RF barcode reader design strategies are presented based on monostatic and bistatic detection methods. Both of these methods can potentially employ pulsed or continuous-sweep excitation. The advantages and disadvantages of each excitation technique are discussed in detail and results presented based on bistatic continuous frequency-sweep excitation. Key words: active tags, antenna monostatic backscatter, bistatic forwardscattering, frequency selective surfaces, ISM, load modulation, microstrip antennas, passive tags, radar cross section, reflectance measurement, transponders transmittance measurement.
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Hudson, Sian Rose. "Towards chemical tagging strategies for proteomics." Thesis, University of York, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.535039.
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Kammergruber, Walter Christian, and Manfred Langen. "Tagging als soziales Bindeglied für Communities." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2014. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-141453.
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Social Tagging und soziale Netzwerke sind zentrale Bausteine des Web 2.0 und Enterprise 2.0. In diesem Beitrag werden die sozialen Aspekte von Social Tagging beleuchtet und ein Ansatz aufgeführt, um in Folksonomies Personen mit ähnlichen Interessen zu finden. Ferner wird ein Tagging-Framework beschrieben, das im Use Case Alexandria im Rahmen des BMWi-Projekts Theseus entstanden ist.
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Choi, Keunwoo. "Deep neural networks for music tagging." Thesis, Queen Mary, University of London, 2018. http://qmro.qmul.ac.uk/xmlui/handle/123456789/46029.
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In this thesis, I present my hypothesis, experiment results, and discussion that are related to various aspects of deep neural networks for music tagging. Music tagging is a task to automatically predict the suitable semantic label when music is provided. Generally speaking, the input of music tagging systems can be any entity that constitutes music, e.g., audio content, lyrics, or metadata, but only the audio content is considered in this thesis. My hypothesis is that we can fi nd effective deep learning practices for the task of music tagging task that improves the classi fication performance. As a computational model to realise a music tagging system, I use deep neural networks. Combined with the research problem, the scope of this thesis is the understanding, interpretation, optimisation, and application of deep neural networks in the context of music tagging systems. The ultimate goal of this thesis is to provide insight that can help to improve deep learning-based music tagging systems. There are many smaller goals in this regard. Since using deep neural networks is a data-driven approach, it is crucial to understand the dataset. Selecting and designing a better architecture is the next topic to discuss. Since the tagging is done with audio input, preprocessing the audio signal becomes one of the important research topics. After building (or training) a music tagging system, fi nding a suitable way to re-use it for other music information retrieval tasks is a compelling topic, in addition to interpreting the trained system. The evidence presented in the thesis supports that deep neural networks are powerful and credible methods for building a music tagging system.
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Silva, Miguel Marinhas da. "Automated image tagging through tag propagation." Master's thesis, Faculdade de Ciências e Tecnologia, 2011. http://hdl.handle.net/10362/5963.
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Trabalho apresentado no âmbito do Mestrado em
Engenharia Informática, como requisito parcial
Para obtenção do grau de Mestre em Engenharia
InformáticaToday, more and more data is becoming available on the Web. In particular, we have recently witnessed an exponential increase of multimedia content within various content sharing websites. While this content is widely available, great challenges have arisen to effectively search and browse such vast amount of content. A solution to this problem is to annotate information, a task that without computer aid requires a large-scale human effort. The goal of this thesis is to automate the task of annotating multimedia information with machine learning algorithms. We propose the development of a machine learning framework capable of doing automated image annotation in large-scale consumer photos. To this extent a study on state of art algorithms was conducted, which concluded with a baseline implementation of a k-nearest neighbor algorithm. This baseline was used to implement a more advanced algorithm capable of annotating images in the situations with limited training images and a large set of test images – thus, a semi-supervised approach. Further studies were conducted on the feature spaces used to describe images towards a successful integration in the developed framework. We first analyzed the semantic gap between the visual feature spaces and concepts present in an image, and how to avoid or mitigate this gap. Moreover, we examined how users perceive images by performing a statistical analysis of the image tags inserted by users. A linguistic and statistical expansion of image tags was also implemented. The developed framework withstands uneven data distributions that occur in consumer datasets, and scales accordingly, requiring few previously annotated data. The principal mechanism that allows easier scaling is the propagation of information between the annotated data and un-annotated data.
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Kammergruber, Walter Christian, and Manfred Langen. "Tagging als soziales Bindeglied für Communities." Technische Universität Dresden, 2009. https://tud.qucosa.de/id/qucosa%3A27962.
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Social Tagging und soziale Netzwerke sind zentrale Bausteine des Web 2.0 und Enterprise 2.0. In diesem Beitrag werden die sozialen Aspekte von Social Tagging beleuchtet und ein Ansatz aufgeführt, um in Folksonomies Personen mit ähnlichen Interessen zu finden. Ferner wird ein Tagging-Framework beschrieben, das im Use Case Alexandria im Rahmen des BMWi-Projekts Theseus entstanden ist.
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Monirabbassi, Azadeh. "Part of speech tagging of Levantine." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2008. http://wwwlib.umi.com/cr/ucsd/fullcit?p1459900.
Full textAbstract:
Thesis (M.S.)--University of California, San Diego, 2008.Title from first page of PDF file (viewed January 5, 2009). Available via ProQuest Digital Dissertations. Includes bibliographical references (p. 42-43).
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Henderson, Jack. "Decay tagging of neutron-deficient 73,74Sr." Thesis, University of York, 2014. http://etheses.whiterose.ac.uk/7053/.
Full textAbstract:
The isospin dependence of the nuclear interaction was probed using energy differences between analogue excited states across the A=74 isobaric triplet. The first spectroscopy of transitions between states in the proton-dripline nuclide, 74Sr was performed using the highly-selective recoil-beta tagging technique with the JUROGAM II + RITU + GREAT setup at the University of Jyvaskyla. Considerable experimental development was required to acheive the requisite level of sensitivity. This included the development of a phosphor-sandwich detector, a finely segmented doublesided silicon-strip detector (DSSD) and a charged-particle veto array (UoYtube). The 2+ to 0+ and 4+ to 2+ transitions were identified through a combination of time and energy gates, and the implementation of a charged-particle veto. A beta-decay half-life was also extracted, in good agreement with a complementary measurement performed at RIKEN. Comparison with Skyrme Hartree-Fock calculations indicates that 74Sr is approximately 1-MeV less bound than previously supposed, implying that both observed excited states are two-proton unbound. Through comparison with published data from the A=74 isobar, triplet-energy differences (TEDs) were determined, indicating a continued need for an additional isotensor, J=0, 100-keV isospin-nonconserving (INC) interaction component, consistent with that found to be required at lower masses. The implication of this result is that such an INC component is mandated across the nuclear landscape, regardless of local nuclear structure effects. Additionally, candidates for the beta-delayed proton decay of 73Sr via 73Rb to 72Kr were observed using the same experimental setup. The lowest energy proton detected is consistent with that which might be expected for the decay of the ground state in 73Rb, allowing for the first measurement of the proton separation energy, Sp. This value is of considerable astrophysical interest since 73Rb is considered to be a waiting point in the rp process.
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Mack, Philipp. "Kalibration neuer 'Flavor Tagging' Algorithmen mittels Bs-Oszillationen Calibration of new flavor tagging algorithms using Bs oscillations /." [S.l. : s.n.], 2007. http://digbib.ubka.uni-karlsruhe.de/volltexte/1000007124.
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Redondo, Pena Roger Lluis. "Tagging and capture hypothesis of synaptic plasticity : the roles of calmodulin kinases and the phenomenon of behavioural tagging." Thesis, University of Edinburgh, 2010. http://hdl.handle.net/1842/4421.
Full textAbstract:
The aims of this thesis were (1) to learn about the identities of the molecules involved in the maintenance of long-term potentiation (LTP), and (2) to develop and test a behavioural paradigm capable of elucidating the interaction between these molecular processes and the persistence of long-term memories. By improving the stability of field recordings in in vitro electrophysiology, it was possible to investigate the molecular processes that determine the long-term changes in synaptic efficacy. In these experiments, the interactions between two convergent inputs onto the same neuronal population in the CA1 region of the hippocampus were monitored for over ten hours. Analytically powerful three-pathway protocols using sequential strong and weak tetanization in varying orders, and test stimulation over long periods of time after LTP-induction, enabled a pharmacological dissociation of potentially distinct roles of the calmodulin kinase (CaMK) pathways in LTP. This places constraints on the mechanisms by which synaptic potentiation, and possibly memories, become stabilized. The experiments show that tag setting is blocked by the CaMK inhibitor KN-93 that, at low concentration primarily blocks CaMKII, whereas a CaMKK inhibitor, STO-609, selectively limits the synthesis or the availability of plasticity related proteins (PRPs). To test whether memories can be subject to modulation by independent experiences, behavioural studies tested the possibility of lengthening the persistence of a relatively weak memory by pairing its induction with an event capable of inducing the synthesis of the required PRPs. Corticosterone-dependent stressful events like a cold swim proved to interfere and weaken spatial memories. On the other hand, the exploration of a novel environment succeeded in rescuing the decay of a weak memory. The effect of the exploration of the novel environment was dependent on NMDA and dopamine receptor activation, as well as protein synthesis. These results are discussed in relation to the synaptic tagging and capture hypothesis and a novel model of the neuronal mechanisms underlying synaptic plasticity is developed from them.
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Padró, Lluís. "A Hybrid Environment for Syntax-Semantic Tagging." Doctoral thesis, Universitat Politècnica de Catalunya, 1998. http://hdl.handle.net/10803/6643.
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The thesis describes the application of the relaxation labelling algorithm to NLP disambiguation. Language is modelled through context constraint inspired on Constraint Grammars. The constraints enable the use of a real value statind "compatibility". The technique is applied to POS tagging, Shallow Parsing and Word Sense Disambigation. Experiments and results are reported. The proposed approach enables the use of multi-feature constraint models, the simultaneous resolution of several NL disambiguation tasks, and the collaboration of linguistic and statistical models.La tesi descriu l'aplicació de l'algorisme d'etiquetat per relaxacio (relaxation labelling) a la desambiguació del llenguatge natural. La llengua es modela mitjançant restriccions de context inspirades en les Constraint Grammars. Les restriccions permeten l'ús d'un valor real que n'expressa la "compatibilitat". La tècnica s'aplica a la desambiguació morfosintàctica (POS tagging), a l'anàlisi sintàctica superficial (Shallow Parsing) i a la desambiguació semàntica (Word Sense Disambigation), i se'n presenten experiments i resultats. L'enfoc proposat permet la utilització de models de restriccions amb trets múltiples, la resolució simultània de diverses tasques de desambiguació del llenguatge natural, i la col·laboració de models linguístics i estadístics.
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Grabalosa, Gándara Marc. "Flavour Tagging developments within the LHCb experiment." Doctoral thesis, Universitat de Barcelona, 2012. http://hdl.handle.net/10803/84088.
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Flavour Tagging at the LHCb experiment is a fundamental tool for the measurement of B oscillations and the study of CP violation. This document explains the development of different tagging techniques and the different strategies used to combine them to determine the flavour of the B meson as precisely as possible. The response of the tagging algorithms also needs to be optimized and calibrated. Both procedures are described using the available LHCb datasets corresponding to various integrated luminosities. First results on the tagging performances are shown for different control channels and physics measurements.
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Strandqvist, Wiktor. "Neural Networks for Part-of-Speech Tagging." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-129296.
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The aim of this thesis is to explore the viability of artificial neural networks using a purely contextual word representation as a solution for part-of-speech tagging. Furthermore, the effects of deep learning and increased contextual information of the network are explored. This was achieved by creating an artificial neural network written in Python. The input vectors employed were created by Word2Vec. This system was compared to a baseline using a tagger with handcrafted features in respect to accuracy and precision. The results show that the use of artificial neural networks using a purely contextual word representation shows promise, but ultimately falls roughly two percent short of the baseline. The suspected reason for this is the suboptimal representation for rare words. The use of deeper network architectures shows an insignificant improvement, indicating that the data sets used might be too small. The use of additional context information provided a higher accuracy, but started to decline after a context size of one.
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Zhang, Jun. "Genotype/Haplotype Tagging Methods and their Validation." Digital Archive @ GSU, 2007. http://digitalarchive.gsu.edu/cs_theses/51.
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This study focuses how the MLR-tagging for statistical covering, i.e. either maximizing average R2 for certain number of requested tags or minimizing number of tags such that for any non-tag SNP there exists a highly correlated (squared correlation R2 > 0.8) tag SNP. We compare with tagger, a software for selecting tags in hapMap project. MLR-tagging needs less number of tags than tagger in all 6 cases of the given test sets except 2. Meanwhile, Biologists can detect or collect data only from a small set. So, this will bring a problem for scientists that the estimates accuracy of tag SNPs when constructing the complete human haplotype map. This study investigates how the MLR-tagging for statistically coverage performs under unbias study. The experiment results shows MLR-tagging still select small amount of SNPs very well even without observing the entire SNP in the sample.
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Davis, Stephen. "Radio frequency tagging in the retail industry." Thesis, University of the West of England, Bristol, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.262824.
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Wightman, Lionel. "Linear epitope tagging of the prion protein." Thesis, University of Reading, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.282977.
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Parisi, D. "Analysis of mRNA 3' tagging pathway components." Thesis, University of Liverpool, 2018. http://livrepository.liverpool.ac.uk/3021109/.
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Fletcher, Richard R. (Richard Ribon). "Low-cost electromagnetic tagging : design and implementation." Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/17615.
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Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2002.Includes bibliographical references (p. 220-222).
Several implementations of chipless RFID (Radio Frequency Identification) tags are presented and discussed as low-cost alternatives to chip-based RFID tags and sensors. An overview of present-day near-field electromagnetic tagging is presented, including both chip-based and chipless technologies with associated costs. As a candidate for low-cost ID tags, a design theory and implementation is presented for multiply-resonant planar metal structures. This theory includes a circuit model, a phenomenological model, and a framework for predicting the resonant frequencies as a function of geometrical and material properties. A novel physical geometry, a tree-like spiral structure, is proposed as a design that increases the number of resonances per unit area in a planar structure relative to the present day state-of-the-art. In addition to identification, it is shown how several chipless tags can also be designed to function as sensors. Several examples are discussed in detail, including: 1) a family of thermal sensor tags employing magnetic materials and 2) a family of sensor tags (to sense pressure, humidity, and pH) based on planar resonator structures. The latter section of the dissertation describes the evolution of my work in developing the necessary (and low-cost) instrumentation to support these new varieties of tag technologies, ranging from a $500 frequency-agile reader to a $5 reader for toy applications.
by Richard Ribon Fletcher.
Ph.D.
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Lin, Winston Huairen. "Extracting ontological structures from collaborative tagging systems." Thesis, School of Information Technologies, 2012. http://hdl.handle.net/2123/12116.
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Kipp, Margaret E. I. "Tagging for health information organisation and retrieval." dLIST, 2007. http://hdl.handle.net/10150/105622.
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This paper examines the tagging practices evident on CiteULike, a research oriented social bookmarking site for journal articles. Articles selected for this study were health information and medicine related. Tagging practices were examined using standard informetric measures for analysis of bibliographic information and analysis of term use. Additionally, tags were compared to descriptors assigned to the same article.
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Lau, Cher Han (Andy). "Structuring free-form tagging in online news." Thesis, Queensland University of Technology, 2009. https://eprints.qut.edu.au/30315/1/Cher_Lau_Thesis.pdf.
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Tagging has become one of the key activities in next generation websites which allow users selecting short labels to annotate, manage, and share multimedia information such as photos, videos and bookmarks. Tagging does not require users any prior training before participating in the annotation activities as they can freely choose any terms which best represent the semantic of contents without worrying about any formal structure or ontology. However, the practice of free-form tagging can lead to several problems, such as synonymy, polysemy and ambiguity, which potentially increase the complexity of managing the tags and retrieving information. To solve these problems, this research aims to construct a lightweight indexing scheme to structure tags by identifying and disambiguating the meaning of terms and construct a knowledge base or dictionary. News has been chosen as the primary domain of application to demonstrate the benefits of using structured tags for managing the rapidly changing and dynamic nature of news information. One of the main outcomes of this work is an automatically constructed vocabulary that defines the meaning of each named entity tag, which can be extracted from a news article (including person, location and organisation), based on experts suggestions from major search engines and the knowledge from public database such as Wikipedia. To demonstrate the potential applications of the vocabulary, we have used it to provide more functionalities in an online news website, including topic-based news reading, intuitive tagging, clipping and sharing of interesting news, as well as news filtering or searching based on named entity tags. The evaluation results on the impact of disambiguating tags have shown that the vocabulary can help to significantly improve news searching performance. The preliminary results from our user study have demonstrated that users can benefit from the additional functionalities on the news websites as they are able to retrieve more relevant news, clip and share news with friends and families effectively.
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Lau, Cher Han (Andy). "Structuring free-form tagging in online news." Queensland University of Technology, 2009. http://eprints.qut.edu.au/30315/.
Full textAbstract:
Tagging has become one of the key activities in next generation websites which allow users selecting short labels to annotate, manage, and share multimedia information such as photos, videos and bookmarks. Tagging does not require users any prior training before participating in the annotation activities as they can freely choose any terms which best represent the semantic of contents without worrying about any formal structure or ontology. However, the practice of free-form tagging can lead to several problems, such as synonymy, polysemy and ambiguity, which potentially increase the complexity of managing the tags and retrieving information. To solve these problems, this research aims to construct a lightweight indexing scheme to structure tags by identifying and disambiguating the meaning of terms and construct a knowledge base or dictionary. News has been chosen as the primary domain of application to demonstrate the benefits of using structured tags for managing the rapidly changing and dynamic nature of news information. One of the main outcomes of this work is an automatically constructed vocabulary that defines the meaning of each named entity tag, which can be extracted from a news article (including person, location and organisation), based on experts suggestions from major search engines and the knowledge from public database such as Wikipedia. To demonstrate the potential applications of the vocabulary, we have used it to provide more functionalities in an online news website, including topic-based news reading, intuitive tagging, clipping and sharing of interesting news, as well as news filtering or searching based on named entity tags. The evaluation results on the impact of disambiguating tags have shown that the vocabulary can help to significantly improve news searching performance. The preliminary results from our user study have demonstrated that users can benefit from the additional functionalities on the news websites as they are able to retrieve more relevant news, clip and share news with friends and families effectively.
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Chen, X. "TAGGING BIOCONTROL STREPTOMYCES TO STUDY LETTUCE COLONIZATION." Doctoral thesis, Università degli Studi di Milano, 2015. http://hdl.handle.net/2434/345187.
Full textAbstract:
The ability of the biological control agents (BCAs) to colonize plant tissues is an important feature involved in microbe-assisted plant protection. Plant-microbe interaction research increased especially in the last decade thanks to technological revolution. Molecular methods and the development of advanced microscopic techniques allow researchers to explore gene expression and localization of beneficial microorganisms within plants. The green fluorescent protein (GFP) and its modified version, enhanced GFP (EGFP), more adapt for expression in mammalian cells and GC-rich actinomycetes like Streptomyces, have been widely used as markers to study gene expression, as well as plant-microbe interactions. Aside fluorescent protein approaches, fluorescence in situ hybridization (FISH) is another frequently used technique to visualize microbial colonization patterns and community composition by application of specific fluorescent probes. Firstly, we transformed five Streptomyces strains, which showed strong inhibition activity against Sclerotinia sclerotiorum, with the EGFP construct by the conjugation method. The conjugation efficiencies varied between the strains, but were comparable to the reference strain. The fitness of transformed strains was similar to wild-type; the transformants maintained similar sporulation, mycelium growth rate, and the ability to produce important secondary metabolites and lytic enzymes. Secondly, two transformed strains, Streptomyces cyaneus ZEA17I, and Streptomyces sp. SW06W, were used to study lettuce colonization dynamics by seed coating method. Their spatio-temporal dynamics were determined in sterile substrate. The strains were consistently recovered from lettuce rhizosphere and inner root tissues up to six weeks. Finally, the colonization pattern of lettuce by Streptomyces cyaneus ZEA17I was examined by both EGFP and FISH approaches combined with confocal laser scanning microscopy (CLSM). For FISH-CLSM analysis, universal bacteria and Streptomyces genus specific probes were used to label S. cyaneus ZEA17I. The consistent presence of the labeled strain at the lettuce root one week after sowing showed that Streptomyces spores could rapidly germinate and produce filamentous mycelium on lettuce. S. cyaneus ZEA17I was detected also on two-week-old roots, indicating the long-term survival ability of this strain in lettuce rhizosphere. Altogether, the antagonistic activity, rhizosphere and root competence showed by the Streptomyces conferred their potential to act as BCA. Further studies on the complex host-pathogen-antagonist interactions will provide additional knowledge to understand the modes and mechanisms of Streptomyces-mediated plant protection.
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Östling, Robert. "Tagging a Morphologically Complex Language Using an Averaged Perceptron Tagger: The Case of Icelandic." Stockholms universitet, Avdelningen för datorlingvistik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-90304.
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In this paper, we experiment with using Stagger, an open-source implementation of an Averaged Perceptron tagger, to tag Icelandic, a morphologically complex language. By adding languagespecific linguistic features and using IceMorphy, an unknown word guesser, we obtain state-of- the-art tagging accuracy of 92.82%. Furthermore, by adding data from a morphological database, and word embeddings induced from an unannotated corpus, the accuracy increases to 93.84%. This is equivalent to an error reduction of 5.5%, compared to the previously best tagger for Icelandic, consisting of linguistic rules and a Hidden Markov Model.
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Pons, Puig Jordi. "Deep neural networks for music and audio tagging." Doctoral thesis, Universitat Pompeu Fabra, 2019. http://hdl.handle.net/10803/668036.
Full textAbstract:
Automatic music and audio tagging can help increase the retrieval and re-use possibilities of many audio databases that remain poorly labeled. In this dissertation, we tackle the task of music and audio tagging from the deep learning perspective and, within that context, we address the following research questions:
(i) Which deep learning architectures are most appropriate for (music) audio signals?
(ii) In which scenarios is waveform-based end-to-end learning feasible?
(iii) How much data is required for carrying out competitive deep learning research?
In pursuit of answering research question (i), we propose to use musically motivated convolutional neural networks as an alternative to designing deep learning models that is based on domain knowledge, and we evaluate several deep learning architectures for audio at a low computational cost with a novel methodology based on non-trained (randomly weighted) convolutional neural networks. Throughout our work, we find that employing music and audio domain knowledge during the model’s design can help improve the efficiency, interpretability, and performance of spectrogram-based deep learning models.
For research questions (ii) and (iii), we perform a study with the SampleCNN, a recently proposed end-to-end learning model, to assess its viability for music audio tagging when variable amounts of training data —ranging from 25k to 1.2M songs— are available. We compare the SampleCNN against a spectrogram-based architecture that is musically motivated and conclude that, given enough data, end-to-end learning models can achieve better results.
Finally, throughout our quest for answering research question (iii), we also investigate whether a naive regularization of the solution space, prototypical networks, transfer learning, or their combination,
can foster deep learning models to better leverage a small number of training examples. Results indicate that transfer learning and prototypical networks are powerful strategies in such low-data regimes.L’etiquetatge automàtic d’àudio i de música pot augmentar les possibilitats de reutilització de moltes de les bases de dades d’àudio que romanen pràcticament sense etiquetar. En aquesta tesi, abordem la tasca de l’etiquetatge automàtic d’àudio i de música des de la perspectiva de l’aprenentatge profund i, en aquest context, abordem les següents qüestions cientı́fiques: (i) Quines arquitectures d’aprenentatge profund són les més adients per a senyals d’àudio (musicals)? (ii) En quins escenaris és viable que els models d’aprenentatge profund processin directament formes d’ona? (iii) Quantes dades es necessiten per dur a terme estudis d’investigació en aprenentatge profund? Per tal de respondre a la primera pregunta (i), proposem utilitzar xarxes neuronals convolucionals motivades musicalment i avaluem diverses arquitectures d’aprenentatge profund per a àudio a un baix cost computacional. Al llarg de les nostres investigacions, trobem que els coneixements previs que tenim sobre la música i l’àudio ens poden ajudar a millorar l’eficiència, la interpretabilitat i el rendiment dels models d’aprenentatge basats en espectrogrames. Per a les preguntes (ii – iii) estudiem com el SampleCNN, un model d’aprenentatge profund que processa formes d’ona, funciona quan disposem de quantitats variables de dades d’entrenament — des de 25k cançons fins a 1’2M cançons. En aquest estudi, comparem el SampleCNN amb una arquitectura basada en espectrogrames que està motivada musicalment. Els resultats experimentals que obtenim indiquen que, en escenaris on disposem de suficients dades, els models d’aprenentatge profund que processen formes d’ona (com el SampleCNN) poden aconseguir millors resultats que els que processen espectrogrames. Finalment, per tal d’intentar respondre a la pregunta (iii), també investiguem si una regularització severa de l’espai de solucions, les xarxes prototipades, l’aprenentatge per transferència de coneixement, o la seva combinació, poden permetre als models d’aprenentatge profund obtenir més bons resultats en escenaris on no hi ha gaires dades d’entrenament. Els resultats dels nostres experiments indiquen que l’aprenentatge per transferència de coneixement i les xarxes prototipades són estratègies útils quan les dades d’entrenament no són abundants.
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Krätzsch, Christine, and Christof Niemann. "Collaborative Tagging - neue Möglichkeiten für die bibliothekarische Sacherschließung." Universitätsbibliothek Chemnitz, 2009. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-200901330.
Full textAbstract:
Die große Freiheit von Tagging-Systemen, wie sie zunehmend auch von
wissenschaftlichen Bibliotheken eingesetzt werden, ist ein ambivalentes Phänomen.
Zum einen bietet das „Tagging“ Kundennähe und beachtliches kreatives Potenzial,
zum anderen erzeugt es jedoch große Mengen völlig unkontrollierter Meta-
Informationen, über deren Qualität noch Unklarheit besteht. Die Universität
Mannheim widmet sich in zwei kooperierenden DFG-Projekten dieser Problematik.
Im Vortrag wurde die Implementierung eines Tagging-Systems im Online-Katalog
sowie die Anwendung einer selbst entwickelten Opensource-Software zur
Qualitätskontrolle von Klassifikationen und Tagging-Daten mittels innovativer
Visualisierungstechnik und statistischer Methoden vorgestellt.
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Dubien, Stephen, and University of Lethbridge Faculty of Arts and Science. "Question answering using document tagging and question classification." Thesis, Lethbridge, Alta. : University of Lethbridge, Faculty of Arts and Science, 2005, 2005. http://hdl.handle.net/10133/248.
Full textAbstract:
Question answering (QA) is a relatively new area of research. QA is retriecing answers to questions rather than information retrival systems (search engines), which retrieve documents. This means that question answering systems will possibly be the next generation of search engines. What is left to be done to allow QA to be the next generation of search engines? The answer is higher accuracy, which can be achieved by investigating methods of questions answering. I took the approach of designing a question answering system that is based on document tagging and question classification. Question classification extracts useful information from the question about how to answer the question. Document tagging extracts useful information from the documents, which will be used in finding the answer to the question. We used different available systems to tage the documents. Our system classifies the questions using manually developed rules. I also investigated different ways which can use both these methods to answer questions and found that our methods had a comparable accuracy to some systems that use deeper processing techniques. This thesis includes investigations into modules of a question answering system and gives insights into how to go about developing a question answering system based on document tagging and question classification. I also evaluated our current system with the questions from the TREC 2004 question answering track.viii, 139 leaves ; 29 cm.
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Monti, Oliver A. L. "Crossing thresholds : Rydberg-tagging and near-threshold photodissociation." Thesis, University of Oxford, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.365747.
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Fazekas, Peter. "Tagging the world : descrying consciousness in cognitive processes." Thesis, University of Edinburgh, 2012. http://hdl.handle.net/1842/7903.
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Although having conscious experiences is a fundamental feature of our everyday life, our understanding of what consciousness is is very limited. According to one of the main conclusions of contemporary philosophy of mind, the qualitative aspect of consciousness seems to resist functionalisation, i.e. it cannot be adequately defined solely in terms of functional or causal roles, which leads to an epistemic gap between phenomenal and scientific knowledge. Phenomenal qualities, then, seem to be, in principle, unexplainable in scientific terms. As a reaction to this pessimistic conclusion it is a major trend in contemporary science of consciousness to turn away from subjective experiences and re-define the subject of investigations in neurological and behavioural terms. This move, however, creates a gap between scientific theories of consciousness, and the original phenomenon, which we are so intimately connected with. The thesis focuses on this gap. It is argued that it is possible to explain features of consciousness in scientific terms. The thesis argues for this claim from two directions. On the one hand, a specific identity theory is formulated connecting phenomenal qualities to certain intermediate level perceptual representations which are unstructured for central processes of the embedding cognitive system. This identity theory is hypothesised on the basis of certain similarities recognised between the phenomenal and the cognitive-representational domains, and then utilised in order to uncover further similarities between these two domains. The identity theory and the further similarities uncovered are then deployed in formulating explanations of the philosophically most important characteristics of the phenomenal domain—i.e. why phenomenal qualities resist functionalisation, and why the epistemic gap occurs. On the other hand, the thesis investigates and criticises existing models of reductive explanation. On the basis of a detailed analysis of how successful scientific explanations proceed a novel account of reductive explanation is proposed, which utilises so-called prior identities. Prior identities are prerequisites rather than outcomes of reductive explanations. They themselves are unexplained but are nevertheless necessary for mapping the features to be explained onto the features the explanation relies on. Prior identities are hypothesised in order to foster the formulation of explanatory claims accounting for target level phenomena in terms of base level processes—and they are justified if they help projecting base level explanations to new territories of the target level. The thesis concludes that the identity theory proposed is a prior identity, and the explanations of features of the phenomenal domain formulated with the aid of this identity are reductive explanations proper. In this sense, the thesis introduces the problem of phenomenal consciousness into scientific discourse, and therefore offers a bridge between the philosophy and the science of consciousness: it offers an approach to conscious experience which, on the one hand, tries to account for the philosophically most important features of consciousness, whereas, on the other hand, does it in a way which smoothly fits into the everyday practice of scientific research.
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Hina, Saman. "Semantic tagging of medical narratives using SNOMED CT." Thesis, University of Leeds, 2013. http://etheses.whiterose.ac.uk/5289/.
Full textAbstract:
In the medical domain, semantic analysis is critical for several research questions which are not only limited to healthcare researchers but are of interest to NLP researchers. Yet, most of the data exists in the form of medical narratives. Semantic analysis of medical narratives is required to be carried out for the identification of semantic information and its classification with semantic categories. This semantic analysis is useful for domain users as well as non-domain users for further investigations. The main objective of this research is to develop a generic semantic tagger for medical narratives using a tag set derived from SNOMED CT® which is an international healthcare terminology. Towards this objective, the key hypothesis is that it is possible to identify semantic information (paraphrases of concepts, abbreviations of concepts and complex multiword concepts) in medical narratives and classify with globally known semantic categories by analysis of an authentic corpus of medical narratives and the language of SNOMED CT®. This research began with an investigation of using SNOMED CT® for identification of concepts in medical narratives which resulted in the derivation of a tag set. Later in this research, this tag set was used to develop three gold standard datasets. One of these datasets required anonymization because it contained four protected health information (PHI) categories. Therefore, a separate module was developed for the anonymization of these PHI categories. After the anonymization, a generic annotation scheme was developed and evaluated for the annotation of three gold standard datasets. One of the gold standard datasets was used to develop generic rule-patterns for the semantic tagger while the other two datasets were used for the evaluation of semantic tagger. Besides evaluation using the gold standard datasets, the semantic tagger was compared with three systems based on different methods, and shown to outperform them.
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Magableh, Murad. "A generic architecture for semantic enhanced tagging systems." Thesis, De Montfort University, 2011. http://hdl.handle.net/2086/5172.
Full textAbstract:
The Social Web, or Web 2.0, has recently gained popularity because of its low cost and ease of use. Social tagging sites (e.g. Flickr and YouTube) offer new principles for end-users to publish and classify their content (data). Tagging systems contain free-keywords (tags) generated by end-users to annotate and categorise data. Lack of semantics is the main drawback in social tagging due to the use of unstructured vocabulary. Therefore, tagging systems suffer from shortcomings such as low precision, lack of collocation, synonymy, multilinguality, and use of shorthands. Consequently, relevant contents are not visible, and thus not retrievable while searching in tag-based systems. On the other hand, the Semantic Web, so-called Web 3.0, provides a rich semantic infrastructure. Ontologies are the key enabling technology for the Semantic Web. Ontologies can be integrated with the Social Web to overcome the lack of semantics in tagging systems. In the work presented in this thesis, we build an architecture to address a number of tagging systems drawbacks. In particular, we make use of the controlled vocabularies presented by ontologies to improve the information retrieval in tag-based systems. Based on the tags provided by the end-users, we introduce the idea of adding “system tags” from semantic, as well as social, resources. The “system tags” are comprehensive and wide-ranging in comparison with the limited “user tags”. The system tags are used to fill the gap between the user tags and the search terms used for searching in the tag-based systems. We restricted the scope of our work to tackle the following tagging systems shortcomings: - The lack of semantic relations between user tags and search terms (e.g. synonymy, hypernymy), - The lack of translation mediums between user tags and search terms (multilinguality), - The lack of context to define the emergent shorthand writing user tags. To address the first shortcoming, we use the WordNet ontology as a semantic lingual resource from where system tags are extracted. For the second shortcoming, we use the MultiWordNet ontology to recognise the cross-languages linkages between different languages. Finally, to address the third shortcoming, we use tag clusters that are obtained from the Social Web to create a context for defining the meaning of shorthand writing tags. A prototype for our architecture was implemented. In the prototype system, we built our own database to host videos that we imported from real tag-based system (YouTube). The user tags associated with these videos were also imported and stored in the database. For each user tag, our algorithm adds a number of system tags that came from either semantic ontologies (WordNet or MultiWordNet), or from tag clusters that are imported from the Flickr website. Therefore, each system tag added to annotate the imported videos has a relationship with one of the user tags on that video. The relationship might be one of the following: synonymy, hypernymy, similar term, related term, translation, or clustering relation. To evaluate the suitability of our proposed system tags, we developed an online environment where participants submit search terms and retrieve two groups of videos to be evaluated. Each group is produced from one distinct type of tags; user tags or system tags. The videos in the two groups are produced from the same database and are evaluated by the same participants in order to have a consistent and reliable evaluation. Since the user tags are used nowadays for searching the real tag-based systems, we consider its efficiency as a criterion (reference) to which we compare the efficiency of the new system tags. In order to compare the relevancy between the search terms and each group of retrieved videos, we carried out a statistical approach. According to Wilcoxon Signed-Rank test, there was no significant difference between using either system tags or user tags. The findings revealed that the use of the system tags in the search is as efficient as the use of the user tags; both types of tags produce different results, but at the same level of relevance to the submitted search terms.