PPT-Cross-Language Retrieval

INST 734 Module 11 Doug Oard Agenda CLIR DictionaryBased CLIR CorpusBased CLIR Interactive CLIR Sources of Translation Knowledge Lexicons Phrase books bilingual dictionaries Similarity. 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. Hui Fang , Tao . Tao. , . ChengXiang. . Zhai. University of Illinois at Urbana Champaign. SIGIR 2004 Best Paper. Presented by Lingjie Zhang. Outline. Formal Definitions of Heuristic Retrieval Constraints. INST 734. Module 11. Doug . Oard. Agenda. CLIR. Dictionary-Based CLIR. Corpus-Based CLIR. Interactive CLIR. Web . Pages. Internet Users. Source: . E. thnologue. (1999). Source: International Monetary Fund (2014). 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.. Gonzalo Gonzalez Abad. Helen Wang. Christopher Miller. Kelly Chance. Xiong. . Liu. Thomas . Kurosu. . OMI Science Team Meeting 12. th. March 2014. Summary. Formaldehyde. Updates to spectroscopy. Slant column fitting. Group 3. Chad Mills. Esad Suskic. Wee Teck Tan. Outline. System and Data. Document Retrieval. Passage Retrieval. Results. Conclusion. System and Data. Development. Testing. TREC 2004. TREC 2004. TREC 2005. 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 . All slides ©Addison Wesley, 2008. Retrieval Models. Provide a mathematical framework for defining the search process. includes explanation of assumptions. basis of many ranking algorithms. can be implicit. Class Activity. Action: . Write. down the . 4 or 5 points. that most impact you from this presentation.. You will need this for the group activity at the end of this presentation.. Key to Learning. What is IR?. Sit down before fact as a little child, . be prepared to give up every conceived notion, . follow humbly wherever and whatever abysses nature leads, . or you will learn nothing. . . -- Thomas Huxley --. 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 .