PPT-Retrieval of Reading Materials for Vocabulary and Reading P

Author : conchita-marotz | Published Date : 2016-12-07

Michael Heilman Le Zhao Juan Pino Maxine Eskenazi Language Technologies Institute Carnegie Mellon University 1 The Goal To help ESL teachers find reading materials

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Retrieval of Reading Materials for Vocabulary and Reading P: Transcript

Michael Heilman Le Zhao Juan Pino Maxine Eskenazi Language Technologies Institute Carnegie Mellon University 1 The Goal To help ESL teachers find reading materials for a particular curriculum or set of students. Background on learning words. Incidental Vocabulary Acquisition. Intentional Vocabulary Instruction. Unknowns in Current Literature. - Literature, tools, and activities will be discussed and reviewed throughout the presentation.. CSC 575. Intelligent Information Retrieval. Intelligent Information Retrieval. 2. Retrieval Models. Model is an idealization or abstraction of an actual process. in this case, process is matching of documents with queries, i.e., retrieval. Pattern Completion and Recapitulation. Episodic Retrieval and the Frontal Lobes. Cues for Retrieval. The Second Time Around: Recognizing Stimuli by Recollection and Familiarity. Misremembering the Past. By . Rong. Yan, Alexander G. and . Rong. Jin. Mwangi. S. . Kariuki. 2008-11629. Quiz. What’s Negative Pseudo-Relevance feedback in multimedia retrieval?. Introduction. As a result of high demand of content based access to video information.. CSC . 575. Intelligent Information Retrieval. 2. Source: . Intel. How much information?. Google: . ~100 . PB a . day; 3+ million servers (15 . Exabytes. stored). Wayback Machine has . ~9 . PB + . 100 . Date :. . 2012 . / . 04. . / . 12. 資訊碩一 . 10077034. 蔡勇儀 . @. . LAB603 . Outline. Introduction. Preliminaries. Method. Experimental result. Conclusions. Introduction. Image retrieval have more challenge than text retrieval.. and Re-ranking. Ling573. NLP Systems and Applications. May 3, 2011. Upcoming Talks. Edith Law. Friday: 3:30; CSE 303. Human Computation: Core Research Questions and Opportunities . Games with a purpose, . ChengXiang. (“Cheng”) . . Zhai. Department of Computer Science. University of Illinois at Urbana-Champaign. http://www.cs.uiuc.edu/homes/czhai. . Email: czhai@illinois.edu. 1. Yahoo!-DAIS Seminar, UIUC. Hongning. Wang. CS@UVa. What is information retrieval?. CS6501: Information Retrieval. CS@UVa. 2. Why information retrieval . Information overload. “. It refers to the . difficulty. a person can have understanding an issue and making decisions that can be caused by the presence of . Hongning. Wang. CS@UVa. Recap: Core IR concepts. Information need. “. an individual or group's desire to locate and obtain information to satisfy a conscious or unconscious need. ” – wiki. An IR system is to satisfy users’ information need. Hongning. Wang. CS@UVa. CS@UVa. CS6501: Information Retrieval. 1. Abstraction of search engine architecture. User. Ranker. Indexer. Doc Analyzer. Index. results. Crawler. Doc . Representation . Query Rep. Fatemeh. Azimzadeh. Books. (Manning et al., 2008). Christopher D. Manning, . Prabhakar. . Raghavan. , and . Hinrich. . Schütze. . Introduction to Information Retrieval. Cambridge University Press, 2008. . Rosalia F. Tungaraza. Advisor: Prof. Linda G. Shapiro. Ph.D. Defense. Computer Science & Engineering. University of Washington. 1. Functional Brain Imaging. Study how the brain works . Imaging while subject performs a task . Dr. S. . Parthasarathy. . MD., DA., DNB, MD (. Acu. ), Dip. . Diab. . DCA, Dip. Software statistics- . PhD ( physiology), IDRA . IVF techniques include:. a. . Ovarian stimulation and monitoring.. b. Ultrasound-directed .

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