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Autor: Grafiati

Data publikacji: 15 lutego 2025

Data aktualizacji: 31 lipca 2025

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1

Ponweiser, Martin. "Latent Dirichlet Allocation in R." WU Vienna University of Economics and Business, 2012. http://epub.wu.ac.at/3558/1/main.pdf.

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Topic models are a new research field within the computer sciences information retrieval and text mining. They are generative probabilistic models of text corpora inferred by machine learning and they can be used for retrieval and text mining tasks. The most prominent topic model is latent Dirichlet allocation (LDA), which was introduced in 2003 by Blei et al. and has since then sparked off the development of other topic models for domain-specific purposes. This thesis focuses on LDA's practical application. Its main goal is the replication of the data analyses from the 2004 LDA paper ``Findi

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Arnekvist, Isac, and Ludvig Ericson. "Finding competitors using Latent Dirichlet Allocation." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-186386.

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Identifying business competitors is of interest to many, but is becoming increasingly hard in an expanding global market. The aim of this report is to investigate whether Latent Dirichlet Allocation (LDA) can be used to identify and rank competitors based on distances between LDA representations of company descriptions. The performance of the LDA model was compared to that of bag-of-words and random ordering by evaluating then comparing them on a handful of common information retrieval metrics. Several different distance metrics were evaluated to determine which metric had best correspondence

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Choubey, Rahul. "Tag recommendation using Latent Dirichlet Allocation." Thesis, Kansas State University, 2011. http://hdl.handle.net/2097/9785.

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Master of Science<br>Department of Computing and Information Sciences<br>Doina Caragea<br>The vast amount of data present on the internet calls for ways to label and organize this data according to specific categories, in order to facilitate search and browsing activities. This can be easily accomplished by making use of folksonomies and user provided tags. However, it can be difficult for users to provide meaningful tags. Tag recommendation systems can guide the users towards informative tags for online resources such as websites, pictures, etc. The aim of this thesis is to build a system

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Risch, Johan. "Detecting Twitter topics using Latent Dirichlet Allocation." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-277260.

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Latent Dirichlet Allocations is evaluated for its suitability when detecting topics in a stream of short messages limited to 140 characters. This is done by assessing its ability to model the incoming messages and its ability to classify previously unseen messages with known topics. The evaluation shows that the model can be suitable for certain applications in topic detection when the stream size is small enough. Furthermoresuggestions on how to handle larger streams are outlined.

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Liu, Zelong. "High performance latent dirichlet allocation for text mining." Thesis, Brunel University, 2013. http://bura.brunel.ac.uk/handle/2438/7726.

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Latent Dirichlet Allocation (LDA), a total probability generative model, is a three-tier Bayesian model. LDA computes the latent topic structure of the data and obtains the significant information of documents. However, traditional LDA has several limitations in practical applications. LDA cannot be directly used in classification because it is a non-supervised learning model. It needs to be embedded into appropriate classification algorithms. LDA is a generative model as it normally generates the latent topics in the categories where the target documents do not belong to, producing the deviat

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Kulhanek, Raymond Daniel. "A Latent Dirichlet Allocation/N-gram Composite Language Model." Wright State University / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=wright1379520876.

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Anaya, Leticia H. "Comparing Latent Dirichlet Allocation and Latent Semantic Analysis as Classifiers." Thesis, University of North Texas, 2011. https://digital.library.unt.edu/ark:/67531/metadc103284/.

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In the Information Age, a proliferation of unstructured text electronic documents exists. Processing these documents by humans is a daunting task as humans have limited cognitive abilities for processing large volumes of documents that can often be extremely lengthy. To address this problem, text data computer algorithms are being developed. Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA) are two text data computer algorithms that have received much attention individually in the text data literature for topic extraction studies but not for document classification nor for

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Jaradat, Shatha. "OLLDA: Dynamic and Scalable Topic Modelling for Twitter : AN ONLINE SUPERVISED LATENT DIRICHLET ALLOCATION ALGORITHM." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-177535.

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Providing high quality of topics inference in today's large and dynamic corpora, such as Twitter, is a challenging task. This is especially challenging taking into account that the content in this environment contains short texts and many abbreviations. This project proposes an improvement of a popular online topics modelling algorithm for Latent Dirichlet Allocation (LDA), by incorporating supervision to make it suitable for Twitter context. This improvement is motivated by the need for a single algorithm that achieves both objectives: analyzing huge amounts of documents, including new docume

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Yalamanchili, Hima Bindu. "A Novel Approach For Cancer Characterization Using Latent Dirichlet Allocation and Disease-Specific Genomic Analysis." Wright State University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=wright1527600876174758.

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Sheikha, Hassan. "Text mining Twitter social media for Covid-19 : Comparing latent semantic analysis and latent Dirichlet allocation." Thesis, Högskolan i Gävle, Avdelningen för datavetenskap och samhällsbyggnad, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-32567.

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In this thesis, the Twitter social media is data mined for information about the covid-19 outbreak during the month of March, starting from the 3’rd and ending on the 31’st. 100,000 tweets were collected from Harvard’s opensource data and recreated using Hydrate. This data is analyzed further using different Natural Language Processing (NLP) methodologies, such as termfrequency inverse document frequency (TF-IDF), lemmatizing, tokenizing, Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA). Furthermore, the results of the LSA and LDA algorithms is reduced dimensional data that

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Nelaturu, Keerthi. "Content Management and Hashtag Recommendation in a P2P Social Networking Application." Thesis, Université d'Ottawa / University of Ottawa, 2015. http://hdl.handle.net/10393/32501.

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In this thesis focus is on developing an online social network application with a Peer-to-Peer infrastructure motivated by BestPeer++ architecture and BATON overlay structure. BestPeer++ is a data processing platform which enables data sharing between enterprise systems. BATON is an open-sourced project which implements a peer-to-peer with a topology of a balanced tree. We designed and developed the components for users to manage their accounts, maintain friend relationships, and publish their contents with privacy control and newsfeed, notification requests in this social network- ing applica

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Järvstråt, Lotta. "Functionality Classification Filter for Websites." Thesis, Linköpings universitet, Statistik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-93702.

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The objective of this thesis is to evaluate different models and methods for website classification. The websites are classified based on their functionality, in this case specifically whether they are forums, news sites or blogs. The analysis aims at solving a search engine problem, which means that it is interesting to know from which categories in a information search the results come. The data consists of two datasets, extracted from the web in January and April 2013. Together these data sets consist of approximately 40.000 observations, with each observation being the extracted text from

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Schenk, Jason Robert. "Meta-uncertainty and resilience with applications in intelligence analysis." The Ohio State University, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=osu1199129269.

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Askling, Kim. "Application of Topic Models for Test Case Selection : A comparison of similarity-based selection techniques." Thesis, Linköpings universitet, Programvara och system, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-159803.

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Regression testing is just as important for the quality assurance of a system, as it is time consuming. Several techniques exist with the purpose of lowering the execution times of test suites and provide faster feedback to the developers, examples are ones based on transition-models or string-distances. These techniques are called test case selection (TCS) techniques, and focuses on selecting subsets of the test suite deemed relevant for the modifications made to the system under test. This thesis project focused on evaluating the use of a topic model, latent dirichlet allocation, as a means

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Malhomme, Nemo. "Statistical learning for climate models." Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPAST165.

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Les modèles climatiques peinent à représenter précisément les structures de circulation atmosphérique liées aux événements extrêmes, et notamment leurs variations régionales.Cette thèse explore comment l'Allocation Latente de Dirichlet (LDA), une méthode d'apprentissage statistique issue du traitement du langage naturel, peut être utilisée pour évaluer la représentation par modèles climatiques de données telles que la pression au niveau de la mer (SLP).La LDA identifie un jeu de structures locales (ou motifs) à l'échelle synoptique, interprétables physiquement comme des cyclones et des anticyc

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Lindgren, Jennifer. "Evaluating Hierarchical LDA Topic Models for Article Categorization." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-167080.

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With the vast amount of information available on the Internet today, helping users find relevant content has become a prioritized task in many software products that recommend news articles. One such product is Opera for Android, which has a news feed containing articles the user may be interested in. In order to easily determine what articles to recommend, they can be categorized by the topics they contain. One approach of categorizing articles is using Machine Learning and Natural Language Processing (NLP). A commonly used model is Latent Dirichlet Allocation (LDA), which finds latent topics

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Morchid, Mohamed. "Représentations robustes de documents bruités dans des espaces homogènes." Thesis, Avignon, 2014. http://www.theses.fr/2014AVIG0202/document.

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En recherche d’information, les documents sont le plus souvent considérés comme des "sacs-de-mots". Ce modèle ne tient pas compte de la structure temporelle du document et est sensible aux bruits qui peuvent altérer la forme lexicale. Ces bruits peuvent être produits par différentes sources : forme peu contrôlée des messages des sites de micro-blogging, messages vocaux dont la transcription automatique contient des erreurs, variabilités lexicales et grammaticales dans les forums du Web. . . Le travail présenté dans cette thèse s’intéresse au problème de la représentation de documents issus de

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Hachey, Benjamin. "Towards generic relation extraction." Thesis, University of Edinburgh, 2009. http://hdl.handle.net/1842/3978.

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A vast amount of usable electronic data is in the form of unstructured text. The relation extraction task aims to identify useful information in text (e.g., PersonW works for OrganisationX, GeneY encodes ProteinZ) and recode it in a format such as a relational database that can be more effectively used for querying and automated reasoning. However, adapting conventional relation extraction systems to new domains or tasks requires significant effort from annotators and developers. Furthermore, previous adaptation approaches based on bootstrapping start from example instances of the target relat

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Paganin, Sally. "Prior-driven cluster allocation in bayesian mixture models." Doctoral thesis, Università degli studi di Padova, 2018. http://hdl.handle.net/11577/3426831.

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There is a very rich literature proposing Bayesian approaches for clustering starting with a prior probability distribution on partitions. Most approaches assume exchangeability, leading to simple representations of such prior in terms of an Exchangeable Partition Probability Function (EPPF). Gibbs-type priors encompass a broad class of such cases, including Dirichlet and Pitman-Yor processes. Even though there have been some proposals to relax the exchangeability assumption, allowing covariate-dependence and partial exchangeability, limited consideration has been given on how to include concr

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Bakharia, Aneesha. "Interactive content analysis : evaluating interactive variants of non-negative Matrix Factorisation and Latent Dirichlet Allocation as qualitative content analysis aids." Thesis, Queensland University of Technology, 2014. https://eprints.qut.edu.au/76535/1/Aneesha_Bakharia_Thesis.pdf.

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This thesis addressed issues that have prevented qualitative researchers from using thematic discovery algorithms. The central hypothesis evaluated whether allowing qualitative researchers to interact with thematic discovery algorithms and incorporate domain knowledge improved their ability to address research questions and trust the derived themes. Non-negative Matrix Factorisation and Latent Dirichlet Allocation find latent themes within document collections but these algorithms are rarely used, because qualitative researchers do not trust and cannot interact with the themes that are automat

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Bui, Quang Vu. "Pretopology and Topic Modeling for Complex Systems Analysis : Application on Document Classification and Complex Network Analysis." Thesis, Paris Sciences et Lettres (ComUE), 2018. http://www.theses.fr/2018PSLEP034/document.

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Les travaux de cette thèse présentent le développement d'algorithmes de classification de documents d'une part, ou d'analyse de réseaux complexes d'autre part, en s'appuyant sur la prétopologie, une théorie qui modélise le concept de proximité. Le premier travail développe un cadre pour la classification de documents en combinant une approche de topicmodeling et la prétopologie. Notre contribution propose d'utiliser des distributions de sujets extraites à partir d'un traitement topic-modeling comme entrées pour des méthodes de classification. Dans cette approche, nous avons étudié deux aspects

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Clavijo, García David Mauricio. "Metodología para el análisis de grandes volúmenes de información aplicada a la investigación médica en Chile." Tesis, Universidad de Chile, 2017. http://repositorio.uchile.cl/handle/2250/146597.

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Magíster en Ingeniería de Negocios con Tecnología de Información<br>El conocimiento en la medicina se ha acumulado en artículos de investigación científica a través del tiempo, por consiguiente, se ha generado un interés creciente en desarrollar metodologías de minería de texto para extraer, estructurar y analizar el conocimiento obtenido de grandes volúmenes de información en el menor tiempo posible. En este trabajo se presenta un una metodología que permite lograr el objetivo anterior utilizando el modelo LDA (Latent Dirichlet Allocation). Esta metodología consiste en 3 pasos: Primero, recon

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Halmann, Marju. "Email Mining Classifier : The empirical study on combining the topic modelling with Random Forest classification." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-14710.

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Filtering out and replying automatically to emails are of interest to many but is hard due to the complexity of the language and to dependencies of background information that is not present in the email itself. This paper investigates whether Latent Dirichlet Allocation (LDA) combined with Random Forest classifier can be used for the more general email classification task and how it compares to other existing email classifiers. The comparison is based on the literature study and on the empirical experimentation using two real-life datasets. Firstly, a literature study is performed to gain ins

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Muñoz, Cancino Ricardo Luis. "Diseño, desarrollo y evaluación de un algoritmo para detectar sub-comunidades traslapadas usando análisis de redes sociales y minería de datos." Tesis, Universidad de Chile, 2013. http://www.repositorio.uchile.cl/handle/2250/112582.

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Magíster en Gestión de Operaciones<br>Ingeniero Civil Industrial<br>Los sitios de redes sociales virtuales han tenido un enorme crecimiento en la última década. Su principal objetivo es facilitar la creación de vínculos entre personas que, por ejemplo, comparten intereses, actividades, conocimientos, o conexiones en la vida real. La interacción entre los usuarios genera una comunidad en la red social. Existen varios tipos de comunidades, se distinguen las comunidades de interés y práctica. Una comunidad de interés es un grupo de personas interesadas en compartir y discutir un tema de interés

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Wedenberg, Kim, and Alexander Sjöberg. "Online inference of topics : Implementation of the topic model Latent Dirichlet Allocation using an online variational bayes inference algorithm to sort news articles." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-222429.

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The client of the project has problems with complex queries and noisewhen querying their stream of ﬁve million news articles per day. Thisresults in much manual work when sorting and pruning the search result of their query. Instead of using direct text matching, the approachof the project was to use a topic model to describe articles in terms oftopics covered and to use this new information to sort the articles. An online version of the topic model Latent Dirichlet Allocationwas implemented using online variational Bayes inference to handlestreamed data. Using 100 dimensions, topics such as s

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Long, Hannah. "Geographic Relevance for Travel Search: The 2014-2015 Harvey Mudd College Clinic Project for Expedia, Inc." Scholarship @ Claremont, 2015. http://scholarship.claremont.edu/scripps_theses/670.

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The purpose of this Clinic project is to help Expedia, Inc. expand the search capabilities it offers to its users. In particular, the goal is to help the company respond to unconstrained search queries by generating a method to associate hotels and regions around the world with the higher-level attributes that describe them, such as “family- friendly” or “culturally-rich.” Our team utilized machine-learning algorithms to extract metadata from textual data about hotels and cities. We focused on two machine-learning models: decision trees and Latent Dirichlet Allocation (LDA). The first appeared

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Dupuy, Christophe. "Inference and applications for topic models." Thesis, Paris Sciences et Lettres (ComUE), 2017. http://www.theses.fr/2017PSLEE055/document.

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La plupart des systèmes de recommandation actuels se base sur des évaluations sous forme de notes (i.e., chiffre entre 0 et 5) pour conseiller un contenu (film, restaurant...) à un utilisateur. Ce dernier a souvent la possibilité de commenter ce contenu sous forme de texte en plus de l'évaluer. Il est difficile d'extraire de l'information d'un texte brut tandis qu'une simple note contient peu d'information sur le contenu et l'utilisateur. Dans cette thèse, nous tentons de suggérer à l'utilisateur un texte lisible personnalisé pour l'aider à se faire rapidement une opinion à propos d'un contenu

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Sathi, Veer Reddy, and Jai Simha Ramanujapura. "A Quality Criteria Based Evaluation of Topic Models." Thesis, Blekinge Tekniska Högskola, Institutionen för programvaruteknik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-13274.

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Context. Software testing is the process, where a particular software product, or a system is executed, in order to find out the bugs, or issues which may otherwise degrade its performance. Software testing is usually done based on pre-defined test cases. A test case can be defined as a set of terms, or conditions that are used by the software testers to determine, if a particular system that is under test operates as it is supposed to or not. However, in numerous situations, test cases can be so many that executing each and every test case is practically impossible, as there may be many const

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Chenghua, Lin. "Probabilistic topic models for sentiment analysis on the Web." Thesis, University of Exeter, 2011. http://hdl.handle.net/10036/3307.

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Sentiment analysis aims to use automated tools to detect subjective information such as opinions, attitudes, and feelings expressed in text, and has received a rapid growth of interest in natural language processing in recent years. Probabilistic topic models, on the other hand, are capable of discovering hidden thematic structure in large archives of documents, and have been an active research area in the field of information retrieval. The work in this thesis focuses on developing topic models for automatic sentiment analysis of web data, by combining the ideas from both research domains. On

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Mungre, Surbhi. "LDA-based dimensionality reduction and domain adaptation with application to DNA sequence classification." Thesis, Kansas State University, 2011. http://hdl.handle.net/2097/8846.

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Master of Science<br>Department of Computing and Information Sciences<br>Doina Caragea<br>Several computational biology and bioinformatics problems involve DNA sequence classification using supervised machine learning algorithms. The performance of these algorithms is largely dependent on the availability of labeled data and the approach used to represent DNA sequences as {\it feature vectors}. For many organisms, the labeled DNA data is scarce, while the unlabeled data is easily available. However, for a small number of well-studied model organisms, large amounts of labeled data are availab

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Johansson, Richard, and Heino Otto Engström. "Topic propagation over time in internet security conferences : Topic modeling as a tool to investigate trends for future research." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-177748.

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When conducting research, it is valuable to find high-ranked papers closely related to the specific research area, without spending too much time reading insignificant papers. To make this process more effective an automated process to extract topics from documents would be useful, and this is possible using topic modeling. Topic modeling can also be used to provide topic trends, where a topic is first mentioned, and who the original author was. In this paper, over 5000 articles are scraped from four different top-ranked internet security conferences, using a web scraper built in Python. From

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Fröjd, Sofia. "Measuring the information content of Riksbank meeting minutes." Thesis, Umeå universitet, Institutionen för fysik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-158151.

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As the amount of information available on the internet has increased sharply in the last years, methods for measuring and comparing text-based information is gaining popularity on financial markets. Text mining and natural language processing has become an important tool for classifying large collections of texts or documents. One field of applications is topic modelling of the minutes from central banks' monetary policy meetings, which tend to be about topics such as"inflation", "economic growth" and "rates". The central bank of Sweden is the Riksbank, which hold 6 annual monetary policy meet

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Greau-Hamard, Pierre-Samuel. "Contribution à l’apprentissage non supervisé de protocoles pour la couche de Liaison de données dans les systèmes communicants, à l'aide des Réseaux Bayésiens." Thesis, CentraleSupélec, 2021. http://www.theses.fr/2021CSUP0009.

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Le monde des télécommunications est en rapide développement, surtout dans le domaine de l'internet des objets; dans un tel contexte, il serait utile de pouvoir analyser n'importe quel protocole inconnu auquel on pourrait se trouver confronté. Dans ce but, l'obtention de la machine d'états et des formats de trames du protocole cible est indispensable. Ces deux éléments peuvent être extraits de traces réseaux et/ou traces d'exécution à l'aide de techniques de Protocol Reverse Engineering (PRE).A l'aide de l'analyse des performances de trois algorithmes utilisés dans des systèmes de PRE, nous avo

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Rocha, João Pedro Magalhães da. "The evolution of international business research : a content analysis of EIBA’s conference papers (1999-2011)." Master's thesis, Instituto Superior de Economia e Gestão, 2020. http://hdl.handle.net/10400.5/20932.

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Mestrado em Economia e Gestão de Ciência, Tecnologia e Inovação<br>Este estudo busca analisar a evolução das Conferências Anuais da European International Business Academy entre os anos 1999 e 2011. Um conjunto de 2221 documentos apresentados durante o período foi processado com o uso de uma ferramenta computadorizada - Latent Dirichlet Allocation, potencializada por sistemas de inteligência artificial - para facilitar a análise de conteúdo. O estudo utilizou o sistema de software R como a plataforma de aplicação do método, com o apoio de bibliotecas compatíveis para auxiliar as etapas de pré-

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Déhaye, Vincent. "Characterisation of a developer’s experience fields using topic modelling." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-171946.

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Finding the most relevant candidate for a position represents an ubiquitous challenge for organisations. It can also be arduous for a candidate to explain on a concise resume what they have experience with. Due to the fact that the candidate usually has to select which experience to expose and filter out some of them, they might not be detected by the person carrying out the search, whereas they were indeed having the desired experience. In the field of software engineering, developing one's experience usually leaves traces behind: the code one produced. This project explores approaches to tac

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Ficapal, Vila Joan. "Anemone: a Visual Semantic Graph." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-252810.

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Semantic graphs have been used for optimizing various natural language processing tasks as well as augmenting search and information retrieval tasks. In most cases these semantic graphs have been constructed through supervised machine learning methodologies that depend on manually curated ontologies such as Wikipedia or similar. In this thesis, which consists of two parts, we explore in the first part the possibility to automatically populate a semantic graph from an ad hoc data set of 50 000 newspaper articles in a completely unsupervised manner. The utility of the visual representation of th

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MAGATTI, DAVIDE. "Graphical models for text mining: knowledge extraction and performance estimation." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2011. http://hdl.handle.net/10281/19576.

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The amount of information produced every day is steadily increasing. The extraction of knowledge from such information is becoming a key aspect for many companies and institutions which spend a great deal of efforts in document management and organization with slightly sufficient results. In this dissertation, we are mainly concerned with probabilistic graphical model for knowledge extraction and performance estimation. In particular, we present a set of models that could improve information mining by relieving the user from boring duties and offering efficient ways to manage, classify,

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Park, Kyoung Jin. "Generating Thematic Maps from Hyperspectral Imagery Using a Bag-of-Materials Model." The Ohio State University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=osu1366296426.

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Russo, Massimiliano. "Bayesian inference for tensor factorization models." Doctoral thesis, Università degli studi di Padova, 2019. http://hdl.handle.net/11577/3426830.

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Multivariate categorical data are routinely collected in several applications, including epidemiology, biology, and sociology, among many others. Popular models dealing with these variables include log-linear and tensor factorization models, with these lasts having the advantage of flexibly characterizing the dependence structure underlying the data. Under such framework, this Thesis aims to provide novel approaches to define compact representations of the dependence structures and to introduce new inference possibilities in tensor factorization approaches. We introduce a new class of GROupe

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Atrevi, Dieudonne Fabrice. "Détection et analyse des évènements rares par vision, dans un contexte urbain ou péri-urbain." Thesis, Orléans, 2019. http://www.theses.fr/2019ORLE2008.

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L’objectif principal de cette thèse est le développement de méthodes complètes de détection d’événements rares. Les travaux de cette thèse se résument en deux parties. La première partie est consacrée à l’étude de descripteurs de formes de l’état de l’art. D’une part, la robustesse de certains descripteurs face à différentes conditions de luminosité a été étudiée. D’autre part, les moments géométriques ont été comparés à travers une application d’estimation de pose humaine 3D à partir d’image 2D. De cette étude, nous avons montré qu’à travers une application de recherche de formes, les moments

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Rusch, Thomas, Paul Hofmarcher, Reinhold Hatzinger, and Kurt Hornik. "Model trees with topic model preprocessing: an approach for data journalism illustrated with the WikiLeaks Afghanistan war logs." Institute of Mathematical Statistics (IMS), 2013. http://dx.doi.org/10.1214/12-AOAS618.

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The WikiLeaks Afghanistan war logs contain nearly 77,000 reports of incidents in the US-led Afghanistan war, covering the period from January 2004 to December 2009. The recent growth of data on complex social systems and the potential to derive stories from them has shifted the focus of journalistic and scientific attention increasingly toward data-driven journalism and computational social science. In this paper we advocate the usage of modern statistical methods for problems of data journalism and beyond, which may help journalistic and scientific work and lead to additional insight.

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Apelthun, Catharina. "Topic modeling on a classical Swedish text corpus of prose fiction : Hyperparameters’ effect on theme composition and identification of writing style." Thesis, Uppsala universitet, Statistiska institutionen, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-441653.

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A topic modeling method, smoothed Latent Dirichlet Allocation (LDA) is applied on a text corpus data of classical Swedish prose fiction. The thesis consists of two parts. In the first part, a smoothed LDA model is applied to the corpus, investigating how changes in hyperparameter values affect the topics in terms of distribution of words within topics and topics within novels. In the second part, two smoothed LDA models are applied to a reduced corpus, only consisting of adjectives. The generated topics are examined to see if they are more likely to occur in a text of a particular author and i

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Cedervall, Andreas, and Daniel Jansson. "Topic classification of Monetary Policy Minutes from the Swedish Central Bank." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-240403.

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Over the last couple of years, Machine Learning has seen a very high increase in usage. Many previously manual tasks are becoming automated and it stands to reason that this development will continue in an incredible pace. This paper builds on the work in Topic Classification and attempts to provide a baseline on how to analyse the Swedish Central Bank Minutes and gather information using both Latent Dirichlet Allocation and a simple Neural Networks. Topic Classification is done on Monetary Policy Minutes from 2004 to 2018 to find how the distributions of topics change over time. The results a

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Schneider, Bruno. "Visualização em multirresolução do fluxo de tópicos em coleções de texto." reponame:Repositório Institucional do FGV, 2014. http://hdl.handle.net/10438/11745.

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Submitted by Bruno Schneider (bruno.sch@gmail.com) on 2014-05-08T17:46:04Z No. of bitstreams: 1 dissertacao_bruno_schneider.pdf.pdf: 8019497 bytes, checksum: 70ff1fddb844b630666397e95c188672 (MD5)<br>Approved for entry into archive by Janete de Oliveira Feitosa (janete.feitosa@fgv.br) on 2014-05-13T12:56:21Z (GMT) No. of bitstreams: 1 dissertacao_bruno_schneider.pdf.pdf: 8019497 bytes, checksum: 70ff1fddb844b630666397e95c188672 (MD5)<br>Approved for entry into archive by Marcia Bacha (marcia.bacha@fgv.br) on 2014-05-14T19:44:51Z (GMT) No. of bitstreams: 1 dissertacao_bruno_schneider.pdf.pd

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Pratt, Landon James. "Cliff Walls: Threats to Validity in Empirical Studies of Open Source Forges." BYU ScholarsArchive, 2013. https://scholarsarchive.byu.edu/etd/3511.

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Artifact-based research provides a mechanism whereby researchers may study the creation of software yet avoid many of the difficulties of direct observation and experimentation. Open source software forges are of great value to the software researcher, because they expose many of the artifacts of software development. However, many challenges affect the quality of artifact-based studies, especially those studies examining software evolution. This thesis addresses one of these threats: the presence of very large commits, which we refer to as "Cliff Walls." Cliff walls are a threat to studies of

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Harrysson, Mattias. "Neural probabilistic topic modeling of short and messy text." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-189532.

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Exploring massive amount of user generated data with topics posits a new way to find useful information. The topics are assumed to be “hidden” and must be “uncovered” by statistical methods such as topic modeling. However, the user generated data is typically short and messy e.g. informal chat conversations, heavy use of slang words and “noise” which could be URL’s or other forms of pseudo-text. This type of data is difficult to process for most natural language processing methods, including topic modeling. This thesis attempts to find the approach that objectively give the better topics from

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Moon, Gordon Euhyun. "Parallel Algorithms for Machine Learning." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1561980674706558.

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Webb, Jared Anthony. "A Topics Analysis Model for Health Insurance Claims." BYU ScholarsArchive, 2013. https://scholarsarchive.byu.edu/etd/3805.

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Mathematical probability has a rich theory and powerful applications. Of particular note is the Markov chain Monte Carlo (MCMC) method for sampling from high dimensional distributions that may not admit a naive analysis. We develop the theory of the MCMC method from first principles and prove its relevance. We also define a Bayesian hierarchical model for generating data. By understanding how data are generated we may infer hidden structure about these models. We use a specific MCMC method called a Gibbs' sampler to discover topic distributions in a hierarchical Bayesian model called Topics Ov

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Victors, Mason Lemoyne. "A Classification Tool for Predictive Data Analysis in Healthcare." BYU ScholarsArchive, 2013. https://scholarsarchive.byu.edu/etd/5639.

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Hidden Markov Models (HMMs) have seen widespread use in a variety of applications ranging from speech recognition to gene prediction. While developed over forty years ago, they remain a standard tool for sequential data analysis. More recently, Latent Dirichlet Allocation (LDA) was developed and soon gained widespread popularity as a powerful topic analysis tool for text corpora. We thoroughly develop LDA and a generalization of HMMs and demonstrate the conjunctive use of both methods in predictive data analysis for health care problems. While these two tools (LDA and HMM) have been used in co

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Chen, Yuxin. "Apprentissage interactif de mots et d'objets pour un robot humanoïde." Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLY003/document.

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Les applications futures de la robotique, en particulier pour des robots de service à la personne, exigeront des capacités d’adaptation continue à l'environnement, et notamment la capacité à reconnaître des nouveaux objets et apprendre des nouveaux mots via l'interaction avec les humains. Bien qu'ayant fait d'énormes progrès en utilisant l'apprentissage automatique, les méthodes actuelles de vision par ordinateur pour la détection et la représentation des objets reposent fortement sur de très bonnes bases de données d’entrainement et des supervisions d'apprentissage idéales. En revanche, les e

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