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Journal articles on the topic 'Statistical topic models'

Author: Grafiati

Published: 4 June 2021

Last updated: 17 February 2022

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1

Tang, Guoyu, Yunqing Xia, Jun Sun, Min Zhang, and Thomas Fang Zheng. "Statistical word sense aware topic models." Soft Computing 19, no. 1 (2014): 13–27. http://dx.doi.org/10.1007/s00500-014-1372-z.

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Yang, Yi, Shimei Pan, Jie Lu, Mercan Topkara, and Yangqiu Song. "The Stability and Usability of Statistical Topic Models." ACM Transactions on Interactive Intelligent Systems 6, no. 2 (2016): 1–23. http://dx.doi.org/10.1145/2954002.

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Rubin, Timothy N., America Chambers, Padhraic Smyth, and Mark Steyvers. "Statistical topic models for multi-label document classification." Machine Learning 88, no. 1-2 (2011): 157–208. http://dx.doi.org/10.1007/s10994-011-5272-5.

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Xiong, Deyi, Fandong Meng, and Qun Liu. "Topic-based term translation models for statistical machine translation." Artificial Intelligence 232 (March 2016): 54–75. http://dx.doi.org/10.1016/j.artint.2015.12.002.

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Koltcov, Sergei, and Vera Ignatenko. "Renormalization Analysis of Topic Models." Entropy 22, no. 5 (2020): 556. http://dx.doi.org/10.3390/e22050556.

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In practice, to build a machine learning model of big data, one needs to tune model parameters. The process of parameter tuning involves extremely time-consuming and computationally expensive grid search. However, the theory of statistical physics provides techniques allowing us to optimize this process. The paper shows that a function of the output of topic modeling demonstrates self-similar behavior under variation of the number of clusters. Such behavior allows using a renormalization technique. A combination of renormalization procedure with the Renyi entropy approach allows for quick sear

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Wright, Jerry H., Michael J. Carey, and Eluned S. Parris. "Topic discrimination using higher-order statistical models of spotted keywords." Computer Speech & Language 9, no. 4 (1995): 381–405. http://dx.doi.org/10.1006/csla.1995.0018.

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Et. al., K. Prashant Gokul,. "AN IMPROVED PATTERN BASED LDA TOPIC MODELING FOR BUSINESS INTELLIGENCE." INFORMATION TECHNOLOGY IN INDUSTRY 9, no. 2 (2021): 404–9. http://dx.doi.org/10.17762/itii.v9i2.363.

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Topic models give a helpful strategy to dimensionality decrease and exploratory data analysis in huge text corpora. Most ways to deal with topic model learning have been founded on a greatest likelihood objective. Proficient algorithms exist that endeavor to inexact this target, yet they have no provable certifications. As of late, algorithms have been presented that give provable limits, however these algorithms are not down to earth since they are wasteful and not hearty to infringement of model presumptions. In this work, we propose to consolidate the statistical topic modeling with pattern

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Koltcov, Sergei, Vera Ignatenko, Zeyd Boukhers, and Steffen Staab. "Analyzing the Influence of Hyper-parameters and Regularizers of Topic Modeling in Terms of Renyi Entropy." Entropy 22, no. 4 (2020): 394. http://dx.doi.org/10.3390/e22040394.

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Topic modeling is a popular technique for clustering large collections of text documents. A variety of different types of regularization is implemented in topic modeling. In this paper, we propose a novel approach for analyzing the influence of different regularization types on results of topic modeling. Based on Renyi entropy, this approach is inspired by the concepts from statistical physics, where an inferred topical structure of a collection can be considered an information statistical system residing in a non-equilibrium state. By testing our approach on four models—Probabilistic Latent S

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Serão, Nick V., Amy L. Petry, and Leticia P. Sanglard. "PSX-41 Late-Breaking Abstract: Self-reported statistical training of graduate students associated with confidence in performing statistical analyses." Journal of Animal Science 98, Supplement_4 (2020): 358. http://dx.doi.org/10.1093/jas/skaa278.627.

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Abstract Training in statistical analysis is essential to Animal Science graduate student curricula. Recently, with the increased use of high-throughput technologies in Animal Science fields, graduate students need certain statistical competencies to manage and analyze so-called “Big Data.” The objective of this study was to identify the current statistical competencies associated with graduate students’ perceived confidence to perform statistical analyses. Data from a comprehensive survey of students enrolled in Animal Science-related graduate programs (n = 354; 125 M.S. and 229 Ph.D. student

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GOTOH, YOSHIHIKO, and STEVE RENALS. "Topic-based mixture language modelling." Natural Language Engineering 5, no. 4 (1999): 355–75. http://dx.doi.org/10.1017/s1351324900002278.

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This paper describes an approach for constructing a mixture of language models based on simple statistical notions of semantics using probabilistic models developed for information retrieval. The approach encapsulates corpus-derived semantic information and is able to model varying styles of text. Using such information, the corpus texts are clustered in an unsupervised manner and a mixture of topic-specific language models is automatically created. The principal contribution of this work is to characterise the document space resulting from information retrieval techniques and to demonstrate t

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Mouhoub, Malek, and Mustakim Al Helal. "Topic Modelling in Bangla Language: An LDA Approach to Optimize Topics and News Classification." Computer and Information Science 11, no. 4 (2018): 77. http://dx.doi.org/10.5539/cis.v11n4p77.

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Topic modeling is a powerful technique for unsupervised analysis of large document collections. Topic models have a wide range of applications including tag recommendation, text categorization, keyword extraction and similarity search in the text mining, information retrieval and statistical language modeling. The research on topic modeling is gaining popularity day by day. There are various efficient topic modeling techniques available for the English language as it is one of the most spoken languages in the whole world but not for the other spoken languages. Bangla being the seventh most spo

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Zhang, M., X. Xiao, D. Xiong, and Q. Liu. "Topic-Based Dissimilarity and Sensitivity Models for Translation Rule Selection." Journal of Artificial Intelligence Research 50 (May 8, 2014): 1–30. http://dx.doi.org/10.1613/jair.4265.

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Translation rule selection is a task of selecting appropriate translation rules for an ambiguous source-language segment. As translation ambiguities are pervasive in statistical machine translation, we introduce two topic-based models for translation rule selection which incorporates global topic information into translation disambiguation. We associate each synchronous translation rule with source- and target-side topic distributions.With these topic distributions, we propose a topic dissimilarity model to select desirable (less dissimilar) rules by imposing penalties for rules with a large v

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Adler, Y., V. Shper, and O. Maksimova. "Assignable causes of variation and statistical models: another approach to an old topic." Quality and Reliability Engineering International 27, no. 5 (2011): 623–28. http://dx.doi.org/10.1002/qre.1207.

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Khan, Muhammad Taimoor, Shehzad Khalid, and Furqan Aziz. "Graph clustering based size varying rules for lifelong topic modelling." Global Journal of Computer Sciences: Theory and Research 8, no. 3 (2018): 111–19. http://dx.doi.org/10.18844/gjcs.v8i3.4021.

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Lifelong learning topic models identify the hidden concepts discussed in the collection of documents. Lifelong learning models have an automatic learning mechanism. In the learning process, the model gets more knowledgeable with experience as it learns from the past in the form of rules. It carries rules to the future and utilises them when a similar scenario arise in the future. The existing lifelong learning topic models heavily rely on statistical measures to learn rules that lead to two limitations. In this research work, we introduce complex networks analysis for learning rules. The rules

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Tabari, Hossein. "Statistical Analysis and Stochastic Modelling of Hydrological Extremes." Water 11, no. 9 (2019): 1861. http://dx.doi.org/10.3390/w11091861.

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Analysis of hydrological extremes is challenging due to their rarity and small sample size and the interconnections between different types of extremes and gets further complicated by an untrustworthy representation of meso-scale processes involved in extreme events by coarse spatial and temporal scale models as well as biased or missing observations due to technical difficulties during extreme conditions. The special issue “Statistical Analysis and Stochastic Modelling of Hydrological Extremes”—motivated by the need to apply and develop innovative stochastic and statistical approaches to anal

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Bassi, Francesca. "Longitudinal models for dynamic segmentation in financial markets." International Journal of Bank Marketing 35, no. 3 (2017): 431–46. http://dx.doi.org/10.1108/ijbm-05-2016-0068.

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Purpose Dynamic market segmentation is a very important topic in many businesses where it is interesting to gain knowledge on the reference market and on its evolution over time. Various papers in the reference literature are devoted to the topic and different statistical models are proposed. The purpose of this paper is to compare two statistical approaches to model categorical longitudinal data to perform dynamic market segmentation. Design/methodology/approach The latent class Markov model identifies a latent variable whose states represent market segments at an initial point in time, custo

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Allahyari, Mehdi, and Krys Kochut. "Using Semantically-Extended LDA Topic Model for Semantic Tagging." International Journal of Semantic Computing 10, no. 04 (2016): 503–25. http://dx.doi.org/10.1142/s1793351x16400183.

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The volume of documents and online resources has been increasing significantly on the Web for many years. Effectively, organizing this huge amount of information has become a challenging problem. Tagging is a mechanism to aggregate information and a great step towards the Semantic Web vision. Tagging aims to organize, summarize, share and search the Web resources in an effective way. One important problem facing tagging systems is to automatically determine the most appropriate tags for Web documents. In this paper, we propose a probabilistic topic model that incorporates DBpedia knowledge int

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Giraldi, Gilson A., Paulo S. Rodrigues, Edson C. Kitani, and Carlos E. Thomaz. "Dimensionality Reduction, Classification and Reconstruction Problems in Statistical Learning Approaches." Revista de Informática Teórica e Aplicada 15, no. 1 (2008): 137–69. http://dx.doi.org/10.22456/2175-2745.6016.

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Statistical learning theory explores ways of estimating functional dependency from a given collection of data. The specific sub-area of supervised statistical learning covers important models like Perceptron, Support Vector Machines (SVM) and Linear Discriminant Analysis (LDA). In this paper we review the theory of such models and compare their separating hypersurfaces for extracting group-differences between samples. Classification and reconstruction are the main goals of this comparison. We show recent advances in this topic of research illustrating their application on face and medica

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Jiang, Yu Ying, and Yong Ming Zhang. "Statistical Inference for a Kind of Nonlinear Regression Model." Advanced Materials Research 616-618 (December 2012): 2149–52. http://dx.doi.org/10.4028/www.scientific.net/amr.616-618.2149.

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As we all know, statistical inference of linear models has been a hot topic of statistical and econometric research. However, in many practical problems, the variable of interest and covariates are often nonlinear relationship. The performance of the statistical inference using linear models model can be very poor. In this paper, the statistical inference of a nonlinear regression model under some additional restricted conditions is investigated. The restricted estimator for the unknown parameter is proposed. Under some mild conditions, the asymptotic normality of the proposed estimator is est

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Schneider, Walter. "Statistical Gate-Delay Modeling with Copulas." Journal of Integrated Circuits and Systems 15, no. 3 (2020): 1–10. http://dx.doi.org/10.29292/jics.v15i3.138.

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The growing impact of process variations on circuit performance has become a major concern for deep-submicron integrated circuit design, resulting in numerous SSTA-algorithms. The acceptance of such algorithms in industry however will be dependent on modeling the real silicon behavior in SSTA. This includes that the statistical gate-delay models must consider arbitrary process variations and dependencies. In this paper, we introduce the innovative concept of Copulas to handle this topic. A complete Matlab based framework starting from process parameter statistics up to the computation of the s

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Cai, Li, and Mark Hansen. "Improving Educational Assessment: Multivariate Statistical Methods." Policy Insights from the Behavioral and Brain Sciences 5, no. 1 (2017): 19–24. http://dx.doi.org/10.1177/2372732217747006.

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This review presents two case studies that illustrate how multivariate statistical modeling applies to the specific goal of improving educational assessment. The first case study involves the development of a new large-scale English language proficiency assessment system (called the English Language Proficiency Assessment for the 21st Century; ELPA21). The second application concerns efforts to quantify student progress in learning using conditional growth models, a topic of current debate about assessment policy. A popular measure, Student Growth Percentile (SGP), is explored through the lens

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Hecking, Tobias, and Loet Leydesdorff. "Can topic models be used in research evaluations? Reproducibility, validity, and reliability when compared with semantic maps." Research Evaluation 28, no. 3 (2019): 263–72. http://dx.doi.org/10.1093/reseval/rvz015.

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AbstractWe replicate and analyze the topic model which was commissioned to King’s College and Digital Science for the Research Evaluation Framework (REF 2014) in the United Kingdom: 6,638 case descriptions of societal impact were submitted by 154 higher-education institutes. We compare the Latent Dirichlet Allocation (LDA) model with Principal Component Analysis (PCA) of document-term matrices using the same data. Since topic models are almost by definition applied to text corpora which are too large to read, validation of the results of these models is hardly possible; furthermore the models

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Koltcov, Sergei, Vera Ignatenko, and Olessia Koltsova. "Estimating Topic Modeling Performance with Sharma–Mittal Entropy." Entropy 21, no. 7 (2019): 660. http://dx.doi.org/10.3390/e21070660.

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Topic modeling is a popular approach for clustering text documents. However, current tools have a number of unsolved problems such as instability and a lack of criteria for selecting the values of model parameters. In this work, we propose a method to solve partially the problems of optimizing model parameters, simultaneously accounting for semantic stability. Our method is inspired by the concepts from statistical physics and is based on Sharma–Mittal entropy. We test our approach on two models: probabilistic Latent Semantic Analysis (pLSA) and Latent Dirichlet Allocation (LDA) with Gibbs sam

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Koltcov, Sergei, Vera Ignatenko, and Sergei Pashakhin. "Fast Tuning of Topic Models: An Application of Rényi Entropy and Renormalization Theory." Proceedings 46, no. 1 (2019): 5. http://dx.doi.org/10.3390/ecea-5-06674.

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In practice, the critical step in building machine learning models of big data (BD) is costly in terms of time and the computing resources procedure of parameter tuning with a grid search. Due to the size, BD are comparable to mesoscopic physical systems. Hence, methods of statistical physics could be applied to BD. The paper shows that topic modeling demonstrates self-similar behavior under the condition of a varying number of clusters. Such behavior allows using a renormalization technique. The combination of a renormalization procedure with the Rényi entropy approach allows for fast searchi

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Yin, Junjun, Xiyun Liu, Jian Yang, Chih-Yuan Chu, and Yang-Lang Chang. "PolSAR Image Classification Based on Statistical Distribution and MRF." Remote Sensing 12, no. 6 (2020): 1027. http://dx.doi.org/10.3390/rs12061027.

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Classification is an important topic in synthetic aperture radar (SAR) image processing and interpretation. Because of speckle and imaging geometrical distortions, land cover mapping is always a challenging task especially in complex landscapes. In this study, we aim to find a robust and efficient method for polarimetric SAR (PolSAR) image classification. The Markov random field (MRF) has been widely used for capturing the spatial-contextual information of the image. In this paper, we firstly introduce two ways to construct the Wishart mixture model and compare their performances using real Po

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Pang, Ning-Ning. "The Bak–Sneppen Model: A Self-Organized Critical Model of Biological Evolution." International Journal of Modern Physics B 11, no. 12 (1997): 1411–44. http://dx.doi.org/10.1142/s0217979297000721.

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Recently, Bak and Sneppen proposed a simple model, the Bak–Sneppen (BS) model, as a coarse-grained description of biological evolution. It has attracted a lot of attention from interdisciplinary statistical physics community, for its simple model definition but extremely rich properties to be explored. The aim of this paper is to give a pedagogical and update review of this fast-developing topic. The emphasis is on the mechanism by which the BS model approaches the self-organized critical state, the universal properties of the system at criticality, and the relation with other topics, such as

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Derungs, A., C. Schuster-Amft, O. Amft, G. Tröster, and J. Seiter. "Daily Life Activity Routine Discovery in Hemiparetic Rehabilitation Patients Using Topic Models." Methods of Information in Medicine 54, no. 03 (2015): 248–55. http://dx.doi.org/10.3414/me14-01-0082.

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Summary Background: Monitoring natural behavior and activity routines of hemiparetic rehabilitation patients across the day can provide valuable progress information for therapists and patients and contribute to an optimized rehabilitation process. In particular, continuous patient monitoring could add type, frequency and duration of daily life activity routines and hence complement standard clinical scores that are assessed for particular tasks only. Machine learning methods have been applied to infer activity routines from sensor data. However, supervised methods require activity annotations

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Lorentzen, Peter, M. Taylor Fravel, and Jack Paine. "Qualitative investigation of theoretical models: the value of process tracing." Journal of Theoretical Politics 29, no. 3 (2016): 467–91. http://dx.doi.org/10.1177/0951629816664420.

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Political scientists frequently use qualitative evidence to support or evaluate the empirical applicability of formal models. Despite this widespread practice, neither the qualitative methods literature nor research on empirically evaluating formal models systematically address the topic. This article makes three contributions to bridge this gap. First, it demonstrates that formal models and qualitative evidence are indeed frequently combined in current research. Second, it shows how process tracing can be as important a tool for empirically assessing models as statistical testing, because mod

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Roberts, Greg, Nancy Scammacca, and Garrett J. Roberts. "Causal Mediation in Educational Intervention Studies." Behavioral Disorders 43, no. 4 (2018): 457–65. http://dx.doi.org/10.1177/0198742917749560.

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Understanding the factors that mediate the effect of educational or behavioral intervention is critical to advancing both research and practice. When properly implemented, mediators add depth to the results of intervention research, indicating why a program works, highlighting ways to enhance its effectiveness, and revealing the elements that are essential to successful implementation. However, many researchers find mediation a difficult topic and struggle to implement it properly in statistical models of effects from between-groups randomized studies. In an effort to bring clarity to the topi

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Romano, Bernardino, and Francesco Zullo. "Models of Urban Land Use in Europe." International Journal of Agricultural and Environmental Information Systems 4, no. 3 (2013): 80–97. http://dx.doi.org/10.4018/ijaeis.2013070105.

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The aim of this paper is to examine available homogenous data on urban conversion of land in Western European countries and determine whether they are suitable to make an international comparison between land policies and management behaviour at the local level. This paper provides some results obtained from currently available information, but it stresses major data production criticalities which hinder the performance of comparable and reliable overall statistical studies. Conclusions stress the need for greater detail in the production of primary data on the features and magnitude of territ

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Tang, Guoyu, Yunqing Xia, Erik Cambria, Peng Jin, and Thomas Fang Zheng. "Document Representation with Statistical Word Senses in Cross-Lingual Document Clustering." International Journal of Pattern Recognition and Artificial Intelligence 29, no. 02 (2015): 1559003. http://dx.doi.org/10.1142/s021800141559003x.

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Cross-lingual document clustering is the task of automatically organizing a large collection of multi-lingual documents into a few clusters, depending on their content or topic. It is well known that language barrier and translation ambiguity are two challenging issues for cross-lingual document representation. To this end, we propose to represent cross-lingual documents through statistical word senses, which are automatically discovered from a parallel corpus through a novel cross-lingual word sense induction model and a sense clustering method. In particular, the former consists in a sense-b

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TÜR, GÖKHAN, DILEK HAKKANI-TÜR, and KEMAL OFLAZER. "A statistical information extraction system for Turkish." Natural Language Engineering 9, no. 2 (2003): 181–210. http://dx.doi.org/10.1017/s135132490200284x.

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This paper presents the results of a study on information extraction from unrestricted Turkish text using statistical language processing methods. In languages like English, there is a very small number of possible word forms with a given root word. However, languages like Turkish have very productive agglutinative morphology. Thus, it is an issue to build statistical models for specific tasks using the surface forms of the words, mainly because of the data sparseness problem. In order to alleviate this problem, we used additional syntactic information, i.e. the morphological structure of the

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Sánchez-Meca, Julio, and Fulgencio Marín-Martínez. "Meta-analysis in psychological research." International Journal of Psychological Research 3, no. 1 (2010): 150–62. http://dx.doi.org/10.21500/20112084.860.

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Meta-analysis is a research methodology that aims to quantitatively integrate the results of a set of empirical studies about a given topic. With this purpose, effect-size indices are obtained from the individual studies and the characteristics of the studies are coded in order to examine their relationships with the effect sizes. Statistical analysis in meta-analysis requires the weighting of each effect estimate as a function of its precision, by assuming a fixed- or a random-effects model. This paper outlines the steps required for carrying out the statistical analyses in a meta-analysis, t

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Ji, Shaoxiong, Celina Ping Yu, Sai-fu Fung, Shirui Pan, and Guodong Long. "Supervised Learning for Suicidal Ideation Detection in Online User Content." Complexity 2018 (September 9, 2018): 1–10. http://dx.doi.org/10.1155/2018/6157249.

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Early detection and treatment are regarded as the most effective ways to prevent suicidal ideation and potential suicide attempts—two critical risk factors resulting in successful suicides. Online communication channels are becoming a new way for people to express their suicidal tendencies. This paper presents an approach to understand suicidal ideation through online user-generated content with the goal of early detection via supervised learning. Analysing users’ language preferences and topic descriptions reveals rich knowledge that can be used as an early warning system for detecting suicid

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LEE, TIMOTHY H., and MING ZHANG. "BIAS CORRECTION AND STATISTICAL TEST FOR DEVELOPING CREDIT SCORING MODEL THROUGH LOGISTIC REGRESSION APPROACH." International Journal of Information Technology & Decision Making 02, no. 02 (2003): 299–311. http://dx.doi.org/10.1142/s0219622003000665.

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A credit scoring model is a statistical model that uses empirical data to predict the creditworthiness of credit applicants. A simple but very powerful approach to developing a credit scoring model is to employ logistic regression. Due to the heterogeneity among the population, segmentation into reasonably homogeneous subpopulations is desirable to enhance model performances. However, one often needs to use unequal sampling ratios across the segments to extract the development sample. Hence, the models developed will be biased unevenly and needed to be adjusted to make score comparisons across

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Nair, Jyothi G., S. Raja, and P. Devapattabiraman. "A Scientometric Assessment of Renewable Biomass Research Output in India." Indian Journal of Information Sources and Services 9, S1 (2019): 72–76. http://dx.doi.org/10.51983/ijiss.2019.9.s1.559.

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This paper provides a quantitative assessment of the scientific literature for mapping the intellectual structure of Renewable Biomass research in India and its scientific development over 20 years (1999-2018). A total of 691 publications were subjected to examination. The study characterizes top (cited and downloaded) papers, citation patterns, most frequent topic clusters, and keywords, and social mentions by country, discipline, and professional status. The results highlight some areas of improvement in this field of study. This study also reveals a lot of well-established topics which are

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SHEN, BO, and YUN LIU. "AN OPINION FORMATION MODEL WITH TWO STAGES." International Journal of Modern Physics C 18, no. 08 (2007): 1231–42. http://dx.doi.org/10.1142/s0129183107011273.

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The opinion formation process is a general phenomenon in community and society. In recent years, several models of opinion formation have been proposed; however, most models mainly focus on describing views exchange between individuals and interaction between individuals and the environment. In this paper, we present an opinion formation model which takes into account the initial state of opinions and the contacting process with topics. The model involves two stages. The first one is the process in which individuals contact a given topic for the first time and the second one is the social inte

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Pannunzi, Mario, Alexis Pérez-Bellido, Alexandre Pereda-Baños, Joan López-Moliner, Gustavo Deco, and Salvador Soto-Faraco. "Deconstructing multisensory enhancement in detection." Journal of Neurophysiology 113, no. 6 (2015): 1800–1818. http://dx.doi.org/10.1152/jn.00341.2014.

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The mechanisms responsible for the integration of sensory information from different modalities have become a topic of intense interest in psychophysics and neuroscience. Many authors now claim that early, sensory-based cross-modal convergence improves performance in detection tasks. An important strand of supporting evidence for this claim is based on statistical models such as the Pythagorean model or the probabilistic summation model. These models establish statistical benchmarks representing the best predicted performance under the assumption that there are no interactions between the two

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Ben Assuli, Ofir. "Implementing combined decision models in healthcare settings: the Simon and Pauker- Kassirer models." Journal of Hospital Administration 3, no. 2 (2013): 10. http://dx.doi.org/10.5430/jha.v3n2p10.

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Background: Modeling medical decision-making has attracted considerable attention over the years, and has become the topic of many investigations. Researchers have attempted to model this critical and extremely complex process from several different angles to enable hospital clinicians to engage in decision-making using empirical tools. Purpose: This paper takes a famous managerial model of decision-making in a non-medical setting and integrates it with a well- known model of medical decision-making to generate a unified illustration of the process. Both models deal with decision-making. Howev

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Sun, Chengai, Liangyu Lv, Gang Tian, Qibo Wang, Xiaoning Zhang, and Lantian Guo. "Leverage Label and Word Embedding for Semantic Sparse Web Service Discovery." Mathematical Problems in Engineering 2020 (March 24, 2020): 1–8. http://dx.doi.org/10.1155/2020/5670215.

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Information retrieval-based Web service discovery approach suffers from the semantic sparsity problem caused by lacking of statistical information when the Web services are described in short texts. To handle this problem, external information is often utilized to improve the discovery performance. Inspired by this, we propose a novel Web service discovery approach based on a neural topic model and leveraging Web service labels. More specifically, words in Web services are mapped into continuous embeddings, and labels are integrated by a neural topic model simultaneously for embodying external

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Chuluunsaikhan, Tserenpurev, Ga-Ae Ryu, Kwan-Hee Yoo, HyungChul Rah, and Aziz Nasridinov. "Incorporating Deep Learning and News Topic Modeling for Forecasting Pork Prices: The Case of South Korea." Agriculture 10, no. 11 (2020): 513. http://dx.doi.org/10.3390/agriculture10110513.

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Knowing the prices of agricultural commodities in advance can provide governments, farmers, and consumers with various advantages, including a clearer understanding of the market, planning business strategies, and adjusting personal finances. Thus, there have been many efforts to predict the future prices of agricultural commodities in the past. For example, researchers have attempted to predict prices by extracting price quotes, using sentiment analysis algorithms, through statistical information from news stories, and by other means. In this paper, we propose a methodology that predicts the

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Shchegoleva, Natalia, and Andrey Varkentin. "Modern Paradigm of Exports Sophistication: Determinants and Models in Japanese Case." Moscow University Economics Bulletin 2020, no. 3 (2020): 141–59. http://dx.doi.org/10.38050/01300105202037.

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Export determinants and their interconnection with technological sophistication and exchange rates are a popular topic in foreign articles. This paper is devoted to relation between export sophistication and exchange rate, including disaggregation of the used data to the country level. Main hypothesis of the research can be formulated as follows: export sophistication moderates the influence of exchange rate risks on export volume. First of all, analytical review of literature over the period of 2009-2019 has been carried out. It includes such topics as influence of trade policy on wages and e

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Rode, Bernd M., and Thomas S. Hofer. "How to access structure and dynamics of solutions: The capabilities of computational methods (Special Topic Article)." Pure and Applied Chemistry 78, no. 3 (2006): 525–39. http://dx.doi.org/10.1351/pac200678030525.

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The progress of computational chemistry in the treatment of liquid systems is outlined, and the combination of the statistical methods (Monte Carlo, MC, and molecular dynamics, MD) with quantum mechanics as the main foundation of this progress is emphasized. The difficulties of experimental studies of liquid systems without having obtained sophisticated theoretical models describing the structural entities and the dynamical behavior of these liquids demonstrate that chemistry research is in a transition phase, where theory and high-performance computing have not only become a valuable suppleme

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Partovi, T., H. Huang, T. Krauß, H. Mayer, and P. Reinartz. "STATISTICAL BUILDING ROOF RECONSTRUCTION FROM WORLDVIEW-2 STEREO IMAGERY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-3/W2 (March 10, 2015): 161–67. http://dx.doi.org/10.5194/isprsarchives-xl-3-w2-161-2015.

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3D building reconstruction from point clouds is an active research topic in remote sensing, photogrammetry and computer vision. Most of the prior research has been done on 3D building reconstruction from LiDAR data which means high resolution and dense data. The interest of this work is 3D building reconstruction from Digital Surface Models (DSM) of stereo image matching of space borne satellite data which cover larger areas than LiDAR datasets in one data acquisition step and can be used also for remote regions. The challenging problem is the noise of this data because of low resolution and m

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Arellano, April Moreno, Wenrui Dai, Shuang Wang, Xiaoqian Jiang, and Lucila Ohno-Machado. "Privacy Policy and Technology in Biomedical Data Science." Annual Review of Biomedical Data Science 1, no. 1 (2018): 115–29. http://dx.doi.org/10.1146/annurev-biodatasci-080917-013416.

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Privacy is an important consideration when sharing clinical data, which often contain sensitive information. Adequate protection to safeguard patient privacy and to increase public trust in biomedical research is paramount. This review covers topics in policy and technology in the context of clinical data sharing. We review policy articles related to ( a) the Common Rule, HIPAA privacy and security rules, and governance; ( b) patients’ viewpoints and consent practices; and ( c) research ethics. We identify key features of the revised Common Rule and the most notable changes since its previous

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IWAMOTO, KAZUKI, TADASHI DOHI, and NAOTO KAIO. "DISCRETE REPAIR-COST LIMIT REPLACEMENT POLICIES WITH/WITHOUT IMPERFECT REPAIR." Asia-Pacific Journal of Operational Research 25, no. 06 (2008): 735–51. http://dx.doi.org/10.1142/s0217595908001985.

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This paper addresses statistical estimation problems of the optimal repair-cost limits minimizing the long-run average costs per unit time in discrete seting. Two discrete repair-cost limit replacement models with/without imperfect repair are considered. We derive the optimal repair-cost limits analytically and develop the statistical non-parametric procedures to estimate them from the complete sample of repair cost. Then the discrete total time on test (DTTT) concept is introduced and applied to propose the resulting estimators. Numerical experiments through Monte Carlo simulation are provide

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Pham, Thi Minh Lanh, Hai Ha Pham, Nguyen Anh Thu Do, and Dinh Hong Le. "Proposed probabilistic models of pipe failure in water distribution system." MATEC Web of Conferences 193 (2018): 02002. http://dx.doi.org/10.1051/matecconf/201819302002.

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All pipes in water supply network are installed underground, so it is difficult to identify pipe failure location during the operation of a system. Prediction of the risk of pipe failure in the water distribution systems is necessary for preparation of reparations and displacement of a pipe network system. Based on the probability of pipe failure, it will be possible to save money and labor cost for water supply companies. Many studies have been conducted on this topic, some of which used experimental models, others used statistical models in which recently many authors used regression model,

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Hassan, Abubakr, Dingfa Huang, Elhadi K. Mustafa, et al. "Statistical inference and residual analysis for the evaluation of datum transformation models developed on 3D coordinate data." Journal of Applied Geodesy 14, no. 1 (2020): 65–75. http://dx.doi.org/10.1515/jag-2019-0027.

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AbstractThe evaluation of geoscience data is a far-reaching topic which cannot be systematically covered. The purpose of inferential statistics is to harness useful information from data for making decisions. This paper conducts in-depth statistical study for the Bursa-Wolf and Molodensky Badekas models of the three-dimensional transformation parameters. We also considered the combined and observation equations scenarios of these methods for the comparative study. Four key indicators are conducted to evaluate the performance of the two transformation models according to the residual results. T

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Cenek, Martin, Rowan Bulkow, Eric Pak, Levi Oyster, Boyd Ching, and Ashika Mulagada. "Semantic Network Analysis Pipeline—Interactive Text Mining Framework for Exploration of Semantic Flows in Large Corpus of Text." Applied Sciences 9, no. 24 (2019): 5302. http://dx.doi.org/10.3390/app9245302.

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Historical topic modeling and semantic concepts exploration in a large corpus of unstructured text remains a hard, opened problem. Despite advancements in natural languages processing tools, statistical linguistics models, graph theory and visualization, there is no framework that combines these piece-wise tools under one roof. We designed and constructed a Semantic Network Analysis Pipeline (SNAP) that is available as an open-source web-service that implements work-flow needed by a data scientist to explore historical semantic concepts in a text corpus. We define a graph theoretic notion of a

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Stein, Petra. "Mixture of Covariance Structure Models to Identify Different Types of Life Style." Methodology 2, no. 3 (2006): 86–99. http://dx.doi.org/10.1027/1614-2241.2.3.86.

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A central topic of empirical social research is the problem of unobserved heterogeneity. To solve this problem at least partially, a statistical model is presented: the finite mixture of conditional mean and covariance structure models. In this approach, the expected values in each component of a mixture may depend on normally or nonnormally distributed regressor variables. The expected value and the covariance matrix in each component of the mixture are parameterized using conditional mean and covariance structure models. Three different procedures for estimating the parameters of these model

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