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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: http://www.jmlr.org/papers/volume3/blei03a/blei03a.pdf (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: https://doi.org/10.1145/1143844.1143859 . 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. http://www.bbc.co.uk/blogs/internet/entries/1d765aa8-600b-4f32-b110-d02fbf7fd379, 25 June, 2015. [5] BBC. BBC forgotten list “sets precedent”. http://www.bbc.com/news/technology-33287758, 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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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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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 http://hdl.handle.net/11234/1-2673 . LINDAT/CLARIN digital library at the Institute

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From Distributional Semantics to Conceptual Spaces: A Novel Computational Method for Concept Creation

., Theories of Memory . Hove: Lawrence Erlbaum Associates. 29–101. Bengio, Y.; Ducharme, R.; Vincent, P.; and Jauvin, C. 2003. A Neural Probabilistic Language Model. Journal of Machine Learning Research 3:1137–1155. Blei, D. M.; Ng, A. Y.; and Jordan, M. I. 2003. Latent Dirichlet Allocation. Journal of Machine Learning Research 3:993–1022. Boden, M. A. 1990. The Creative Mind: Myths and Mechanisms . London: Weidenfeld and Nicolson. Brown, P. F.; deSouza, P. V.; Mercer, R. L.; Della Pietra, V. J.; and Lai, J. C. 1992. Class-Based n-gram Models of

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A Case Study in Text Mining of Discussion Forum Posts: Classification with Bag of Words and Global Vectors

). Automatic parametric fault detection in complex analog systems based on a method of minimum node selection, International Journal of Applied Mathematics and Computer Science 26(3): 655-668, DOI: 10.1515/amcs-2016-0045. Blei, D.M., Ng, A.Y. and Jordan, M.I. (2003). Latent Dirichlet allocation, Journal of Machine Learning Research 3: 993-1022. Breiman, L. (1996). Bagging predictors, Machine Learning 24(2): 123-140. Breiman, L. (2001). Random forests, Machine Learning 45(1): 5-32. Breiman, L., Friedman, J

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Topics of Controversy: An Empirical Analysis of Web Censorship Lists

, Edward Loper, and Ewan Klein. Natural Language Processing with Python. O’Reilly Media Inc., 2009. [11] David M. Blei, Andrew Y. Ng, and Michael I. Jordan. “Latent Dirichlet Allocation.” Journal of Machine Learning Research 3 (2003), pp. 993-1022. [12] Jordan L. Boyd-Graber and David M. Blei. “Multilingual Topic Models for Unaligned Text.” Uncertainty in Artificial Intelligence. Ed. by Jeff A. Bilmes and Andrew Y. Ng. AUAI Press, 2009, pp. 75-82. [13] Leo Breiman. “Random Forests.” Machine Learning 45.1 (2001), pp. 5

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