Relevant bibliographies by topics / LDA MODEL

Academic literature on the topic 'LDA MODEL'

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Published: 11 September 2023

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Journal articles on the topic "LDA MODEL"

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Pang, Xiongwen, Benshuai Wan, Huifang Li, and Weiwei Lin. "MR-LDA." International Journal of Grid and High Performance Computing 8, no. 4 (October 2016): 100–113. http://dx.doi.org/10.4018/ijghpc.2016100106.

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Latent Dirichlet Allocation(LDA) is an efficient method of text mining,but applying LDA directly to Chinese micro-blog texts will not work well because micro-blogs are more social, brief, and closely related with each other. Based on LDA, this paper proposes a Micro-blog Relation LDA model (MR-LDA), which takes the relations between Chinese micro-blog documents and other Chinese micro-blog documents into consideration to help topic mining in micro-blog. The authors extend LDA in the following two points. First, they aggregate several Chinese micro-blogs as a single micro-blog document to solve the problem of short texts. Second, they model the generation process of Chinese micro-blogs more accurately by taking relationship between micro-blog documents into consideration. MR-LDA is more suitable to model Chinese micro-blog data. Gibbs sampling method is borrowed to inference the model. Experimental results on actual datasets show that MR-LDA model can offer an effective solution to text mining for Chinese micro-blog.
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Aslanyan, Tatev Karen, and Flavius Frasincar. "LDA-LFM." ACM SIGAPP Applied Computing Review 21, no. 2 (June 2021): 33–47. http://dx.doi.org/10.1145/3477127.3477130.

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Most of the existing recommender systems are based only on the rating data, and they ignore other sources of information that might increase the quality of recommendations, such as textual reviews, or user and item characteristics. Moreover, the majority of those systems are applicable only on small datasets (with thousands of observations) and are unable to handle large datasets (with millions of observations). We propose a recommender algorithm that combines a rating modeling technique (i.e., Latent Factor Model) with a topic modeling method based on textual reviews (i.e., Latent Dirichlet Allocation), and we extend the algorithm such that it allows adding extra user- and item-specific information to the system. We evaluate the performance of the algorithm using Amazon.com datasets with different sizes, corresponding to 23 product categories. After comparing the built model to four other models, we found that combining textual reviews with ratings leads to better recommendations. Moreover, we found that adding extra user and item features to the model increases its prediction accuracy, which is especially true for medium and large datasets.
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Zhang, Yongjun, Zijian Wang, Yongtao Yu, Bolun Chen, Jialin Ma, and Liang Shi. "LF-LDA." International Journal of Data Warehousing and Mining 14, no. 2 (April 2018): 18–36. http://dx.doi.org/10.4018/ijdwm.2018040102.

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This article describes how text documents are a major data structure in the era of big data. With the explosive growth of data, the number of documents with multi-labels has increased dramatically. The popular multi-label classification technology, which is usually employed to handle multinomial text documents, is sensitive to the noise terms of text documents. Therefore, there still exists a huge room for multi-label classification of text documents. This article introduces a supervised topic model, named labeled LDA with function terms (LF-LDA), to filter out the noisy function terms from text documents, which can help to improve the performance of multi-label classification of text documents. The article also shows the derivation of the Gibbs Sampling formulas in detail, which can be generalized to other similar topic models. Based on the textual data set RCV1-v2, the article compared the proposed model with other two state-of-the-art multi-label classifiers, Tuned SVM and labeled LDA, on both Macro-F1 and Micro-F1 metrics. The result shows that LF-LDA outperforms them and has the lowest variance, which indicates the robustness of the LF-LDA classifier.
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Ekinci, Ekin, and Sevinç İlhan Omurca. "Concept-LDA: Incorporating Babelfy into LDA for aspect extraction." Journal of Information Science 46, no. 3 (April 29, 2019): 406–18. http://dx.doi.org/10.1177/0165551519845854.

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Latent Dirichlet allocation (LDA) is one of the probabilistic topic models; it discovers the latent topic structure in a document collection. The basic assumption under LDA is that documents are viewed as a probabilistic mixture of latent topics; a topic has a probability distribution over words and each document is modelled on the basis of a bag-of-words model. The topic models such as LDA are sufficient in learning hidden topics but they do not take into account the deeper semantic knowledge of a document. In this article, we propose a novel method based on topic modelling to determine the latent aspects of online review documents. In the proposed model, which is called Concept-LDA, the feature space of reviews is enriched with the concepts and named entities, which are extracted from Babelfy to obtain topics that contain not only co-occurred words but also semantically related words. The performance in terms of topic coherence and topic quality is reported over 10 publicly available datasets, and it is demonstrated that Concept-LDA achieves better topic representations than an LDA model alone, as measured by topic coherence and F-measure. The learned topic representation by Concept-LDA leads to accurate and an easy aspect extraction task in an aspect-based sentiment analysis system.
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SHI, Jing, Ming HU, Xin SHI, and Guo-Zhong DAI. "Text Segmentation Based on Model LDA." Chinese Journal of Computers 31, no. 10 (October 16, 2009): 1865–73. http://dx.doi.org/10.3724/sp.j.1016.2008.01865.

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Peng, Qing Xi. "Classifying Sentiment Based on LDA Model." Applied Mechanics and Materials 427-429 (September 2013): 2614–17. http://dx.doi.org/10.4028/www.scientific.net/amm.427-429.2614.

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Online reviews as a new textual domain offer a unique proposition for sentiment analysis. Their short document length suggests any sentiment they contain is compact and explicit. Although supersized methods have obtained good results, a large amount of corpus should be trained beforehand. Recently, topic models have been introduced for the simultaneous analysis for sentiment in the document. However, the LDA model makes the assumption that, given the parameters the words in the document are all independent. It obviously isnt the case. The words in the document express the sentiment of the author. This paper proposes a model to solve the problem. We assume that the sentiments are related to the topic in the documents. A sentiment layer is added to the LDA model to improve it. Experimental result in the dataset demonstrates the advantage of the proposed model.
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SHI, Jing, Meng FAN, and Wan-Long LI. "Topic Analysis Based on LDA Model." Acta Automatica Sinica 35, no. 12 (March 12, 2010): 1586–92. http://dx.doi.org/10.3724/sp.j.1004.2009.01586.

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Cho, Taemin, and Jee-Hyong Lee. "Latent Keyphrase Extraction Using LDA Model." Journal of Korean Institute of Intelligent Systems 25, no. 2 (April 25, 2015): 180–85. http://dx.doi.org/10.5391/jkiis.2015.25.2.180.

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ZHAI, LIDONG, ZHAOYUN DING, YAN JIA, and BIN ZHOU. "A WORD POSITION-RELATED LDA MODEL." International Journal of Pattern Recognition and Artificial Intelligence 25, no. 06 (September 2011): 909–25. http://dx.doi.org/10.1142/s0218001411008890.

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LDA (Latent Dirichlet Allocation) proposed by Blei is a generative probabilistic model of a corpus, where documents are represented as random mixtures over latent topics, and each topic is characterized by a distribution over words, but not the attributes of word positions of every document in the corpus. In this paper, a Word Position-Related LDA Model is proposed taking into account the attributes of word positions of every document in the corpus, where each word is characterized by a distribution over word positions. At the same time, the precision of the topic-word's interpretability is improved by integrating the distribution of the word-position and the appropriate word degree, taking into account the different word degree in the different word positions. Finally, a new method, a size-aware word intrusion method is proposed to improve the ability of the topic-word's interpretability. Experimental results on the NIPS corpus show that the Word Position-Related LDA Model can improve the precision of the topic-word's interpretability. And the average improvement of the precision in the topic-word's interpretability is about 9.67%. Also, the size-aware word intrusion method can interpret the topic-word's semantic information more comprehensively and more effectively through comparing the different experimental data.
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Zhou, Xiuze, and Shunxiang Wu. "Rating LDA model for collaborative filtering." Knowledge-Based Systems 110 (October 2016): 135–43. http://dx.doi.org/10.1016/j.knosys.2016.07.020.

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Dissertations / Theses on the topic "LDA MODEL"

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Pereira, Alexandre Miguel Martins. "Relatório de estágio na Ameise Editora LDA." Master's thesis, Universidade de Aveiro, 2015. http://hdl.handle.net/10773/15784.

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Mestrado em Estudos Editoriais
O presente relatório propõe-se a expor todo o meu trabalho desenvolvido durante o período do estágio curricular na Ameise Editora desde agosto de 2014 até fevereiro de 2015. No mesmo encontra-se uma breve história da editora, um enquadramento do trabalho que desenvolvi e como este se relacionou com as unidades curriculares.
This report intends to expose all my work during the traineeship period in Ameise Editora from August 2014 until February 2015. In this same report you will able to read the brief history of Ameise Editora, the framework of the work I have done and how this was related to the master’s subjects.
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Shamsolahi, S. "LDA investigation of flow in a stable mixed-flow pump model." Thesis, University of Strathclyde, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.382495.

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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 short and messy text in a comparative study. The compared approaches are latent Dirichlet allocation (LDA), Re-organized LDA (RO-LDA), Gaussian Mixture Model (GMM) with distributed representation of words, and a new approach based on previous work named Neural Probabilistic Topic Modeling (NPTM). It could only be concluded that NPTM have a tendency to achieve better topics on short and messy text than LDA and RO-LDA. GMM on the other hand could not produce any meaningful results at all. The results are less conclusive since NPTM suffers from long running times which prevented enough samples to be obtained for a statistical test.
Att utforska enorma mängder användargenererad data med ämnen postulerar ett nytt sätt att hitta användbar information. Ämnena antas vara “gömda” och måste “avtäckas” med statistiska metoder såsom ämnesmodellering. Dock är användargenererad data generellt sätt kort och stökig t.ex. informella chattkonversationer, mycket slangord och “brus” som kan vara URL:er eller andra former av pseudo-text. Denna typ av data är svår att bearbeta för de flesta algoritmer i naturligt språk, inklusive ämnesmodellering. Det här arbetet har försökt hitta den metod som objektivt ger dem bättre ämnena ur kort och stökig text i en jämförande studie. De metoder som jämfördes var latent Dirichlet allocation (LDA), Re-organized LDA (RO-LDA), Gaussian Mixture Model (GMM) with distributed representation of words samt en egen metod med namnet Neural Probabilistic Topic Modeling (NPTM) baserat på tidigare arbeten. Den slutsats som kan dras är att NPTM har en tendens att ge bättre ämnen på kort och stökig text jämfört med LDA och RO-LDA. GMM lyckades inte ge några meningsfulla resultat alls. Resultaten är mindre bevisande eftersom NPTM har problem med långa körtider vilket innebär att tillräckligt många stickprov inte kunde erhållas för ett statistiskt test.
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Schreiber, Jonah. "Tracking Online Trend Locations using a Geo-Aware Topic Model." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-190887.

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In automatically categorizing massive corpora of text, various topic models have been applied with good success. Much work has been done on applying machine learning and NLP methods on Internet media, such as Twitter, to survey online discussion. However, less focus has been placed on studying how geographical locations discussed in online fora evolve over time, and even less on associating such location trends with topics. Can online discussions be geographically tracked over time? This thesis attempts to answer this question by evaluating a geo-aware Streaming Latent Dirichlet Allocation (SLDA) implementation which can recognize location terms in text. We show how the model can predict time-dependent locations of the 2016 American primaries by automatic discovery of election topics in various Twitter corpora, and deduce locations over time.
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Carreira, Filipa Contreiras Duarte. "Análise e otimização da gestão de stocks: caso de estudo de uma estação de serviço da Repsol Portuguesa Lda." Master's thesis, Instituto Superior de Economia e Gestão, 2021. http://hdl.handle.net/10400.5/23348.

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Mestrado Bolonha em Gestão e Estratégia Industrial
Num mercado competitivo como o atual, as empresas são forçadas a estabelecer métodos mais eficazes para gerirem os seus stocks uma vez que a gestão dos mesmos é um dos processos mais importantes para o sucesso das organizações. Uma das mais valias deste processo é a diminuição do nível de stock que por sua vez tem um impacto positivo no desempenho organizacional e na redução dos custos totais das empresas. O objetivo deste estudo é analisar e otimizar a gestão de stocks de uma Estação de Serviço da Repsol Portuguesa de forma a minimizar os seus custos anuais de stock. A Repsol é uma empresa multienergética internacional nascida em Espanha e com presença notória em Portugal desde 1990. Este relatório de dissertação pretende ser uma contribuição prática para encontrar o equilíbrio entre a compra e a procura dos produtos vendidos numa Estação de Serviço da Repsol Portuguesa e permitir, deste modo, uma melhor tomada de decisão. Neste estudo serão analisados os modelos de controlo e classificação de stocks e posteriormente aplicado o modelo da Quantidade Económica de Encomenda (QEE). O modelo da QEE define qual a quantidade ótima de encomenda, de forma a minimizar os custos associados ao stock. No final do estudo, é alcançada uma otimização da gestão de stocks, ou seja, existe a possibilidade de redução do custo total do stock em 39,69% com base nos modelos académicos aplicados.
In a competitive market as the current one, companies are forced to establish more efficient methods to manage their stocks, since stock management is one of the most important processes for the success of organizations. One of the advantages of this process is the reduction in the stock level which in turn has a positive impact on organizational performance and on the reduction of the companies’ total cost. The purpose of this study is to analyze and optimize the stock management of a Repsol Portuguesa Gas Station to minimize its annual stock costs. Repsol is an international multi-energy company born in Spain with a notable presence in Portugal since 1990. This dissertation report aims to be a practical contribution by finding the balance between the purchase and the demand for the products sold at one Repsol Portuguesa Gas Station thus allowing better decision making. In this study the stock control and classification models will be analyzed and then the Economic Order Quantity (QEE) model will be applied. The QEE model defines the optimal order quantity that should be ordered to minimize the costs associated with stock. At the end of the study, an optimization of stock management is achieved, meaning that there is a possibility of reducing the stock total cost by 39,69% based on the applied academic models.
info:eu-repo/semantics/publishedVersion
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Saberi, Nasseri Robin. "Stochastic EM for generic topic modeling using probabilistic programming." Thesis, Uppsala universitet, Statistiska institutionen, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-447568.

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Probabilistic topic models are a versatile class of models for discovering latent themes in document collections through unsupervised learning. Conventional inferential methods lack the scaling capabilities necessary for extensions to large-scale applications. In recent years Stochastic Expectation Maximization has proven scalable for the simplest topic model: Latent Dirichlet Allocation. Performing analytical maximization is unfortunately not possible for many more complex topic models. With the rise of probabilistic programming languages, the ability to infer flexibly specified probabilistic models using sophisticated numerical optimization procedures has become widely available. These frameworks have however mainly been developed for optimization of continuous parameters, often prohibiting direct optimization of discrete parameters. This thesis explores the potential of utilizing probabilistic programming for generic topic modeling using Stochastic Expectation Maximization with numerical maximization of discrete parameters reparameterized to unconstrained space. The method achieves results of similar quality as other methods for Latent Dirichlet Allocation in simulated experiments. Further application is made to infer a Dirichlet-multinomial Regression model with metadata covariates. A real dataset is used and the method produces interpretable topics.
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Rosner, Helge. "Electronic structure and exchange integrals of low-dimensional cuprates." [S.l. : s.n.], 1999. http://www.bsz-bw.de/cgi-bin/xvms.cgi?SWB9470690.

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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 within large datasets of for example text articles. An extension of LDA is hierarchical Latent Dirichlet Allocation (hLDA) which is an hierarchical variant of LDA. In hLDA, the latent topics found among a set of articles are structured hierarchically in a tree. Each node represents a topic, and the levels represent different levels of abstraction in the topics. A further extension of hLDA is constrained hLDA, where a set of predefined, constrained topics are added to the tree. The constrained topics are extracted from the dataset by grouping highly correlated words. The idea of constrained hLDA is to improve the topic structure derived by a hLDA model by making the process semi-supervised. The aim of this thesis is to create a hLDA and a constrained hLDA model from a dataset of articles provided by Opera. The models should then be evaluated using the novel metric word frequency similarity, which is a measure of the similarity between the words representing the parent and child topics in a hierarchical topic model. The results show that word frequency similarity can be used to evaluate whether the topics in a parent-child topic pair are too similar, so that the child does not specify a subtopic of the parent. It can also be used to evaluate if the topics are too dissimilar, so that the topics seem unrelated and perhaps should not be connected in the hierarchy. The results also show that the two topic models created had comparable word frequency similarity scores. None of the models seemed to significantly outperform the other with regard to the metric.
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Svensson, Karin, and Johan Blad. "Exploring NMF and LDA Topic Models of Swedish News Articles." Thesis, Uppsala universitet, Avdelningen för systemteknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-429250.

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The ability to automatically analyze and segment news articles by their content is a growing research field. This thesis explores the unsupervised machine learning method topic modeling applied on Swedish news articles for generating topics to describe and segment articles. Specifically, the algorithms non-negative matrix factorization (NMF) and the latent Dirichlet allocation (LDA) are implemented and evaluated. Their usefulness in the news media industry is assessed by its ability to serve as a uniform categorization framework for news articles. This thesis fills a research gap by studying the application of topic modeling on Swedish news articles and contributes by showing that this can yield meaningful results. It is shown that Swedish text data requires extensive data preparation for successful topic models and that nouns exclusively and especially common nouns are the most suitable words to use. Furthermore, the results show that both NMF and LDA are valuable as content analysis tools and categorization frameworks, but they have different characteristics, hence optimal for different use cases. Lastly, the conclusion is that topic models have issues since they can generate unreliable topics that could be misleading for news consumers, but that they nonetheless can be powerful methods for analyzing and segmenting articles efficiently on a grand scale by organizations internally. The thesis project is a collaboration with one of Sweden’s largest media groups and its results have led to a topic modeling implementation for large-scale content analysis to gain insight into readers’ interests.
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Rachman, Luciano. "Modelagem de perdas com ações trabalhistas em instituições financeiras." reponame:Repositório Institucional do FGV, 2013. http://hdl.handle.net/10438/11096.

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As perdas trabalhistas nas Instituições Financeiras representam um valor considerável que devem ser consideradas no modelo de capital regulatório para risco operacional, segundo Basileia. A presente dissertação demonstra uma forma de mensurar o risco às quais as Instituições Financeiras estão expostas nesse tipo de perdas. Diversos tipos de distribuições são analisados conforme sua aderência tanto na frequência como na severidade das perdas. Para os valores de frequência, foi obtida uma amostra de dados real, enquanto para a severidade foram utilizados valores obtidos de relatórios de instituto de pesquisa que serviram de insumo para os cálculos de ações trabalhistas conforme legislação brasileira vigente na CLT (Consolidação das Leis do Trabalho).
According to Basel, the labor losses in Financial Institutions represent a substantial value that should be regarded in the model of regulatory capital for operational risk. This dissertation demonstrates a way to measure the risk to which Financial Institutions are exposed to in this type of loss. Several types of distributions are analyzed according to their adherence both in frequency and severity of losses. For frequency values, it was obtained a sample of actual data, whilst for the severity were used values given from reports of research institute which served as an input for the calculations of labor actions according to the present Brazilian legislation in CLT (Consolidation of Labor Laws).
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Books on the topic "LDA MODEL"

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United States. National Aeronautics and Space Administration., ed. LDV measurements in an annular combustor model. West Lafayette, Ind: Purdue University, School of Aeronautics and Astronautics, 1989.

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United States. National Aeronautics and Space Administration., ed. LDV measurements in an annular combustor model. [Washington, DC]: National Aeronautics and Space Administration, 1996.

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1954-, Milkman Ruth, Bloom Joshua, and Narro Victor 1963-, eds. Working for justice: The L.A. model of organizing and advocacy. Ithaca: Cornell University Press, 2010.

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Canlas, Dante B. Labor force participation in an LDC. [Diliman, Quezon City, Philippines]: University of the Philippines, School of Economics, 1990.

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Selvanathan, E. Antony. International consumption comparisons: OECD versus LDC. Singapore: World Scientific, 2002.

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Saroja, Selvanathan, ed. International consumption comparisons: OECD versus LDC. Singapore: World Scientific, 2003.

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Esguerra, E. F. Trader-lenders in the rural LDC credit market. [Quezon City]: University of the Philippines, School of Economics, 1990.

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Cohen, Daniel. A valuation formula for LDC debt. Washington, DC (1818 H St., NW, Washington 20433): International Economics Dept., World Bank, 1991.

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Dicks, M. J. Secondary market prices of LDC debt. London: Economics Division, Bank of England, 1991.

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Top-model. Montréal: SMBi, 1997.

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Book chapters on the topic "LDA MODEL"

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Drissi, Amani, Ahmed Khemiri, Salma Sassi, Anis Tissaoui, Richard Chbeir, and Abderrazek Jemai. "LDA+: An Extended LDA Model for Topic Hierarchy and Discovery." In Recent Challenges in Intelligent Information and Database Systems, 14–26. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-8234-7_2.

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Lehečka, Jan, and Aleš Pražák. "Online LDA-Based Language Model Adaptation." In Text, Speech, and Dialogue, 334–41. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00794-2_36.

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Gai, Fangyu, Zhiqiang Li, Xinwen Jiang, and Hongchen Guo. "Enhance AdaBoost Algorithm by Integrating LDA Topic Model." In Data Mining and Big Data, 27–37. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-40973-3_3.

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Ai, Zhou, Zhang Yijia, Wei Hao, and Lu Mingyu. "LDA-Transformer Model in Chinese Poetry Authorship Attribution." In Lecture Notes in Computer Science, 59–73. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-88189-4_5.

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Xiao, Yunpeng, Liangyun Liu, Ming Xu, Haohan Wang, and Yanbing Liu. "GLDA-FP: Gaussian LDA Model for Forward Prediction." In Big Data – BigData 2018, 124–39. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-94301-5_10.

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Sun, Muzhen, and Haonan Zheng. "Topic Detection for Post Bar Based on LDA Model." In Communications in Computer and Information Science, 136–49. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2206-8_13.

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Xiong, Caiquan, Li Shen, and Zhuang Wang. "An Automatic Text Summary Method Based on LDA Model." In Advances on P2P, Parallel, Grid, Cloud and Internet Computing, 163–73. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-33509-0_15.

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Zhang, Yongjun, Jialin Ma, Zijian Wang, and Bolun Chen. "LF-LDA: A Topic Model for Multi-label Classification." In Advances in Internetworking, Data & Web Technologies, 618–28. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59463-7_62.

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Li, Zhisheng, Chong Wang, Xing Xie, Xufa Wang, and Wei-Ying Ma. "Exploring LDA-Based Document Model for Geographic Information Retrieval." In Lecture Notes in Computer Science, 842–49. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-85760-0_108.

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He, Min-fan. "A Stock Recommendation Strategy Based on M-LDA Model." In Web Technologies and Applications, 122–28. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-45835-9_11.

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Conference papers on the topic "LDA MODEL"

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Shi, Min, Jianxun Liu, Dong Zhou, Mingdong Tang, and Buqing Cao. "WE-LDA: A Word Embeddings Augmented LDA Model for Web Services Clustering." In 2017 IEEE International Conference on Web Services (ICWS). IEEE, 2017. http://dx.doi.org/10.1109/icws.2017.9.

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Jianyong Duan, Yamin Ai, and Xia li. "LDA topic model for microblog recommendation." In 2015 International Conference on Asian Language Processing (IALP). IEEE, 2015. http://dx.doi.org/10.1109/ialp.2015.7451562.

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Sun, Yan. "The collocation model based on LDA." In Sixth International Conference on Electronics and Information Engineering, edited by Qiang Zhang. SPIE, 2015. http://dx.doi.org/10.1117/12.2202328.

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Yang, Qingquan, and Weijiang Li. "The LDA Topic Model Extension Study." In International Conference on Logistics Engineering, Management and Computer Science (LEMCS 2015). Paris, France: Atlantis Press, 2015. http://dx.doi.org/10.2991/lemcs-15.2015.169.

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Zhao, Fangyuan, Xuebin Ren, Shusen Yang, and Xinyu Yang. "On Privacy Protection of Latent Dirichlet Allocation Model Training." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/675.

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Latent Dirichlet Allocation (LDA) is a popular topic modeling technique for discovery of hidden semantic architecture of text datasets, and plays a fundamental role in many machine learning applications. However, like many other machine learning algorithms, the process of training a LDA model may leak the sensitive information of the training datasets and bring significant privacy risks. To mitigate the privacy issues in LDA, we focus on studying privacy-preserving algorithms of LDA model training in this paper. In particular, we first develop a privacy monitoring algorithm to investigate the privacy guarantee obtained from the inherent randomness of the Collapsed Gibbs Sampling (CGS) process in a typical LDA training algorithm on centralized curated datasets. Then, we further propose a locally private LDA training algorithm on crowdsourced data to provide local differential privacy for individual data contributors. The experimental results on real-world datasets demonstrate the effectiveness of our proposed algorithms.
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Zhang, Maoyuan, Fang Yuan, and Jianping Zhu. "Integrating semantic knowledge into Tag-LDA model through cloud model." In 2015 IEEE International Conference on Big Data (Big Data). IEEE, 2015. http://dx.doi.org/10.1109/bigdata.2015.7364118.

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Chen, Xue, Wenqing Tang, Hao Xu, and Xiaofeng Hu. "Double LDA: A Sentiment Analysis Model Based on Topic Model." In 2014 Tenth International Conference on Semantics, Knowledge and Grids (SKG). IEEE, 2014. http://dx.doi.org/10.1109/skg.2014.20.

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Xie, Lingyun, Ying Shi, and Zhenwei Li. "Driving Pattern Recognition Based on Improved LDA Model." In 2018 5th IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS). IEEE, 2018. http://dx.doi.org/10.1109/ccis.2018.8691348.

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Binkley, David, and Daniel Heinz. "Entropy as a lens into LDA model understanding." In 2017 Computing Conference. IEEE, 2017. http://dx.doi.org/10.1109/sai.2017.8252216.

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Kasliwal, Bhavesh, Shraey Bhatia, Shubham Saini, I. Sumaiya Thaseen, and Ch Aswani Kumar. "A hybrid anomaly detection model using G-LDA." In 2014 IEEE International Advance Computing Conference (IACC). IEEE, 2014. http://dx.doi.org/10.1109/iadcc.2014.6779336.

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Reports on the topic "LDA MODEL"

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Moreno Pérez, Carlos, and Marco Minozzo. “Making Text Talk”: The Minutes of the Central Bank of Brazil and the Real Economy. Madrid: Banco de España, November 2022. http://dx.doi.org/10.53479/23646.

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This paper investigates the relationship between the views expressed in the minutes of the meetings of the Central Bank of Brazil’s Monetary Policy Committee (COPOM) and the real economy. It applies various computational linguistic machine learning algorithms to construct measures of the minutes of the COPOM. First, we create measures of the content of the paragraphs of the minutes using Latent Dirichlet Allocation (LDA). Second, we build an uncertainty index for the minutes using Word Embedding and K-Means. Then, we combine these indices to create two topic-uncertainty indices. The first one is constructed from paragraphs with a higher probability of topics related to “general economic conditions”. The second topic-uncertainty index is constructed from paragraphs that have a higher probability of topics related to “inflation” and the “monetary policy discussion”. Finally, we employ a structural VAR model to explore the lasting effects of these uncertainty indices on certain Brazilian macroeconomic variables. Our results show that greater uncertainty leads to a decline in inflation, the exchange rate, industrial production and retail trade in the period from January 2000 to July 2019.
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Asati, R., X. Liu, S. Esale, X. Chen, and H. Shah. YANG Data Model for MPLS LDP. Edited by K. Raza. RFC Editor, March 2022. http://dx.doi.org/10.17487/rfc9070.

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Cusanelli, Dominic S., and Scott A. Carpenter. Axial Waterjet (AxWJ) Model 5662: Hull Resistance and Model-Scale Powering with LDV Nozzle Design. Fort Belvoir, VA: Defense Technical Information Center, September 2007. http://dx.doi.org/10.21236/ada473572.

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Skone, Timothy J. Natural Gas and Power LCA Model Documentation (NG Report). Office of Scientific and Technical Information (OSTI), May 2014. http://dx.doi.org/10.2172/1526721.

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Zeilenga, K., ed. Lightweight Directory Access Protocol (LDAP): Directory Information Models. RFC Editor, June 2006. http://dx.doi.org/10.17487/rfc4512.

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Chernozhukov, Victor, Kengo Kato, and Alexandre Belloni. Uniform post selection inference for LAD regression models. Cemmap, June 2013. http://dx.doi.org/10.1920/wp.cem.2013.2413.

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Avendaño A, Marilu, Julio Cesar Montoya R, and Martha Lucía Fuertes D. Mejoramiento de la gestión gerencial de la empresa Transportes Unidos de Colombia-Transucol Ltda de Palmira, mediante la implantación del modelo Balance Scoreboard (Cuadro de Mando Integral). Universidad Nacional Abierta y a Distancia, 2016. http://dx.doi.org/10.22490/ecacen.1907.

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Hadgu, Teklu, Gordon Appel, Timothy Kotoski, and Chad Wilkes. FY19 Status Report on the Computing Systems for the Yucca Mountain Project TSPA-LA Models and Preliminary Testing of a Selected Process Model. Office of Scientific and Technical Information (OSTI), February 2020. http://dx.doi.org/10.2172/1763267.

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Méndez-Vizcaíno, Juan C., Alexander Guarín, César Anzola-Bravo, and Anderson Grajales-Olarte. Characterizing and Communicating the Balance of Risks of Macroeconomic Forecasts: A Predictive Density Approach for Colombia. Banco de la República, October 2021. http://dx.doi.org/10.32468/be.1178.

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Since July 2021, Banco de la República strengthened its forecasting process and communication instruments, by involving predictive densities on the projections of its models, PATACON and 4GM. This paper presents the main theoretical and empirical elements of the predictive density approach for macroeconomic forecasting. This model-based methodology allows to characterize the balance of risks of the economy, and quantify their effects through a joint probability distribution of forecasts. We estimate this distribution based on the simulation of DSGE models, preserving the general equilibrium relationships and their macroeconomic consistency. We also illustrate the technical criteria used to represent the prospective factors of risk through the probability distributions of shocks.
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Baltagi, Badi H., Georges Bresson, Anoop Chaturvedi, and Guy Lacroix. Robust dynamic space-time panel data models using ε-contamination: An application to crop yields and climate change. CIRANO, January 2023. http://dx.doi.org/10.54932/ufyn4045.

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This paper extends the Baltagi et al. (2018, 2021) static and dynamic ε-contamination papers to dynamic space-time models. We investigate the robustness of Bayesian panel data models to possible misspecification of the prior distribution. The proposed robust Bayesian approach departs from the standard Bayesian framework in two ways. First, we consider the ε-contamination class of prior distributions for the model parameters as well as for the individual effects. Second, both the base elicited priors and the ε-contamination priors use Zellner (1986)’s g-priors for the variance-covariance matrices. We propose a general “toolbox” for a wide range of specifications which includes the dynamic space-time panel model with random effects, with cross-correlated effects `a la Chamberlain, for the Hausman-Taylor world and for dynamic panel data models with homogeneous/heterogeneous slopes and cross-sectional dependence. Using an extensive Monte Carlo simulation study, we compare the finite sample properties of our proposed estimator to those of standard classical estimators. We illustrate our robust Bayesian estimator using the same data as in Keane and Neal (2020). We obtain short run as well as long run effects of climate change on corn producers in the United States.
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