PPT-Topic 5

Lesson 2 The Senate Day 3 The Senate Starter What would be more prestigious a small college that is hard to get into or a large college that is easy to get into Objective Students will learn . Authors: Rosen-. Zvi. , Griffiths, . Steyvers. , Smyth . Venue: the 20th Conference on Uncertainty in Artificial Intelligence. Year: 2004. Presenter: Peter Wu. Date: Apr 7, 2015. Title: The Author-Topic Model for Authors and Documents. David Kauchak. cs160. Fall . 2009. Administrative. Hw5/paper review. what . would be useful for the . authors. technical. , refers to not only the results, but the rest of the . paper. give . as many specific examples as . Siddharth Gupta. 1. , Casey Hanson. 2. , Carl A Gunter. 3. , Mario Frank. 4. , David Liebovitz. 4. , Bradley . Malin. 6. 1,2,3,4. Department of Computer Science, . 3,5. Department of Medicine, . 6. Department of Biomedical Informatics. Chenghua. Lin . & . Yulan. He. CIKM09. Main Idea. This . paper . proposes . a novel probabilistic modeling framework based on . Latent . Dirichlet. Allocation (LDA), called joint sentiment/. Liangjie Hong. and Brian D. Davison. Computer Science and Engineering. Lehigh University. Bethlehem, PA USA. SOMA 2010 . Why. . we care about text modeling in Twitter ?. SOMA 2010 . Why. . we care about text modeling in Twitter ?. RIMC Research Capacity Enhancement Workshops Series : “Achieving Research Impact”. How to choose a research topic. Stuff to consider:. What are the pressing problems in your domain?. Reflect on your life experiences . 1. . Series Title: “LEARNING TO RECOGNISE AND REMOVE BARRIERS TO HEALING”. . TOPIC No. 2. . “THE BARRIERS . COMING FROM . WRONG ATTITUDES . TO OTHER PEOPLE”. Topic 2. Canon Jim Holbeck. 2. Source: “Topic models”, David . Blei. , MLSS ‘09. Topic modeling - Motivation. Discover topics from a corpus . Model connections between topics . Model the evolution of topics over time . Image annotation. part 1. Andrea Tagarelli. Univ. of Calabria, Italy. Statistical topic modeling. . (1/3). Key assumption: . text . data represented as a mixture . of . topics. , i.e., probability distributions . over . ChengXiang. . Zhai. Department of Computer Science. University of Illinois at Urbana-Champaign. http://www.cs.uiuc.edu/homes/czhai. 1. Search is a means to the end of finishing a task . Decision Making. Padhraic Smyth. Department of Computer Science. University of California, Irvine . . Progress Report. New deadline. In class, Thursday February 18. th. (not Tuesday). Outline. 3 to 5 pages maximum. in an HDP-Based Rating Regression Model for Online Reviews. Zheng Chen. 1. , Yong Zhang. 1,. 2. , Yue Shang. 1. , Xiaohua Hu. 1. 1. Drexel University, USA. 2. China Central Normal University, China. Illustrations, descriptions or procedures. (with prompting). K. Compare & Contrast. The most important points presented by two texts on the same topic. 2. nd. . Integrate. Information from two texts on the same topic in order to write or speak about the subject knowledgably. Here are some examples that you may use: . Should school uniforms be enforced everywhere? . Should . men get paternity leave from work. ? . Are . we too dependent on computers. ?. . Should animals be used for research.