PPT-1 Topic 6 (Ch. 11)

The Efficient Market Hypothesis The efficient market hypothesis Event studies Are markets efficient Weakform t ests Predictability in stock market returns . The speci c topic you select may arise from a number of different triggers If you are a nursing student your interest in a topic may result from a lecture or module on a medical condition that was covered in your undergraduate classes or a medical p TOPIC SENTENCE. A . topic sentence tells the reader what the paragraph is all about.. TOPIC AND MAIN POINT. Every good topic sentence has two parts:. 1. Topic . 2. Main . Point. TOPIC. A topic is WHO the paragraph or story is about.. Paul N. Bennett, Microsoft Research. Joint with. Ece Kamar, Microsoft Research. Gabriella Kazai, Microsoft Research Cambridge. Motivation for Consensus Task. Recover actual . relevance of . a topic-document . UWC Writing Workshop. Spring 2014. Thesis Statements. What do you already know about thesis statements?. Introduction to Thesis Statements . A thesis statement:. Tells the reader how you will interpret the significance of the subject matter under discussion.. 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. 0. Pre-industrial economies in transition. Topics:. Advanced Organic Economies . A characterisation of pre-industrial economies . Demographic forces and economic development. Causes and effects of population change in pre-industrial economies. 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 ?. 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 . Jie Tang. *. , Limin Yao. #. , and Dewei Chen. *. *. Dept. of Computer Science and Technology. Tsinghua University. #. Dept. of Computer Science, University of Massachusetts Amherst. April, 2009. ?. What are the major topics in the returned docs?. Information Ordering. Ling 573. Systems and Applications. May 5, 2016. Roadmap . Entity-based cohesion model:. Model entity . based transitions. Topic-based cohesion model:. Models sequence of topic transitions. Paul N. Bennett, Microsoft Research. Joint with. Ece Kamar, Microsoft Research. Gabriella Kazai, Microsoft Research Cambridge. Motivation for Consensus Task. Recover actual . relevance of . a topic-document .