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A Lexical Approach to Estimating Environmental Goods and Services Output in the Construction Sector via Soft Classification of Enterprise Activity Descriptions Using Latent Dirichlet Allocation

9. References Blei, D., A.Y. Ng, and M. Jordan. 2003. “Latent Dirichlet Allocation.” Journal of Machine Learning Research 3: 993–1022. Available at: (accessed May 2016). Blei, D. and J. Lafferty. 2006. “Dynamic Topic Models.” Proceedings of the 23rd International Conference on Machine Learning, 113–120, Pittsburgh, Pennsylvania, U.S.A., June 25 – 29, 2006. Doi: . Blei, D. and J. Lafferty. 2007. “A Correlated Topic Model of Science.” Annals of

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The Right to be Forgotten in the Media: A Data-Driven Study

’s search results., 25 June, 2015. [5] BBC. BBC forgotten list “sets precedent”., 26 June, 2015. [6] Bert-Jaap Koops. Forgetting footprints, shunning shadows: A critical analysis of the “Right to be Forgotten” in big data practice. SCRIPTed, 8(3):229-256, Dec. 2011. [7] D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent Dirichlet Allocation. the Journal of machine Learning research, 3:993-1022, 2003

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An Influence Prediction Model for Microblog Entries on Public Health Emergencies

research works on microblog influence are abundant. However, research on the influence of microblog in specific fields, such as public health emergencies, is relatively insufficient. This study attempts to propose a microblog influence prediction model for public health emergencies, which is composed of user, time, and content features and which uses the random forest method ( Breiman, 2001 ) and the Best Match 25-based latent Dirichlet allocation model (LDA-BM25) ( Li, 2013 ). As this model is constructed specifically for public health emergencies, it highlights the

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An Indirected Recommendation Model for Chinese Microblog

-325. 4. Blei, D. M., A. Y. Ng, M. I. Jordan. Latent Dirichlet Allocation. - Journal of Machine Learning Research, Vol. 3, 2003, No 4, pp. 993-1022. 5. Liu, Q., H. Ma, E. Chen, H. Xiong. A Survey of Context-Aware Mobile Recommendations. - International Journal of Information Technology and Decision Making, Vol. 12, 2013, No 1, pp. 139-172. 6. Pan, Y., L. Luo, D. Liu. How to Recommend by Online Lifestyle Tagging. - International Journal of Information Technology and Decision Making, Vol. 13, 2014, No 6, pp. 1183-1209. 7

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Journalism and the political structure
The local media system in Norway

analysis of the level of localism and journalistic professionalism in the Norwegian local media system. The analysis is based on structural analysis as well as a mix of descriptive and predictive statistical analyses on a corpus of 847,487 digital news articles collected from 156 online newspapers in 2015–2017, using Latent Dirichlet Allocation (LDA) topic modelling. The extent to which these assumptions are supported in turn enables a discussion of how local media system features contribute to media systems theory. In the following, we first discuss the relevant

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Filtering and Classifying Relevant Short Text with a Few Seed Words

collection of pseudo-documents. Such hidden topics serve as the auxiliary knowledge to regulate the topic learning process in SSCF. On two real-world datasets in two languages, experimental results show that the proposed SSCF consistently achieves better classification accuracy than state-of-the-art dataless baselines in terms of F 1 . We also observe that SSCF can even achieve superior performance to supervised classifiers supervised latent dirichlet allocation (sLDA) and support vector machine (SVM) on some specific tasks. To summarize, the main contributions of this

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Understanding the Correlations between Social Attention and Topic Trends of Scientific Publications

included. PubMed data and Google Trends time-series data can be matched. Since Google Trends data can be provided weekly and PubMed data are released monthly, we convert all weekly data to monthly by taking a four-week moving average. For every selected topic discussed above, we obtain Google Trends time-series data from January 2004 to January 2013. 2.2 Methodology The overall framework of the methodology is shown in Figure 2 , including generating topics from the obesity corpus using the latent Dirichlet allocation (LDA) algorithm ( Blei, Ng, & Jordan, 2003

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Project Zeus: Video Based Behavioural Modelling of Non-Linear Transportation System for Improved Planning &Urban Construction Projects

information processing systems, 18, 147, 2006. [5] Blei, D. M., Ng, A. Y., Jordan, M. I., Latent Dirichlet allocation , Journal of Machine Learning Research, 3, 993-1022, 2003. [6] Rai, A., Artificial Intelligence for Emotion Recognition , Journal of Artificial Intelligence Research & Advances, 1(2), 24-30, 2014. [7] Rai, A., Sakkaravarthi Ramanathan, Kannan, R. J., Quasi Opportunistic Supercomputing for Geospatial Socially Networked Mobile Devices , Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE), IEEE 25th International

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Identifying Different Meanings of a Chinese Morpheme through Semantic Pattern Matching in Augmented Minimum Spanning Trees

References Berge, C. Graphs and hypergraphs. North-Holland Pub. Co., 1976. Blei, D.M. and J.D. Lafferty. A correlated topic model of science. Annals of Applied Statistics , 1 (1):17-35, 2007. Blei, D.M., A.Y. Ng, and M.I. Jordan. Latent Dirichlet allocation. The Journal of Machine Learning Research , 3:993-1022, 2003. Blei, D., T.L. Griffiths, M.I. Jordan, and J.B. Tenenbaum. Hierarchical topic models and the nested Chinese restaurant process. Advances

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Improving Topic Coherence Using Entity Extraction Denoising

’06) , pages 113–120, August 2006. Blei, D. M., A. Ng, and M. I. Jordan. Latent Dirichlet Allocation. Journal of Machine Learning Research , 3:993–1022, 2003. Blei, D. M., T. L. Griffiths, and M. I. Jordan. The nested Chinese restaurant process and Bayesian nonparametric inference of topic hierarchies. Journal of the ACM , 57:7.1–7.30, 2007. Cardenas Acosta, Ronald, Kevin Bello Medina, Alberto Coronado, and Elizabeth Villota. Engineering job ads corpus, 2016. URL . LINDAT/CLARIN digital library at the Institute

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