PPT-Lecture 11: Relevance Feedback & Query Expansion - II

1 2 Take away today Interactive relevance feedback improve initial retrieval results by telling the IR system which docs are relevant nonrelevant Best known relevance feedback method . Exploring . Intrinsic Diversity . in . Web Search . Karthik Raman (Cornell University). Paul N. Bennett (MSR, Redmond). Kevyn. . Collins-Thompson (MSR, Redmond). Whole-Session Relevance. Typical search model : . Multimedia Databases. via Relevance Feedback . with History and Foresight Support. DBRank. 08, April 12. th. 2008, . Cancún. , Mexico. Marc Wichterich. , Christian Beecks, Thomas Seidl. Outline. Motivation. 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.. 2011-11709. Seo. . Seok. . Jun. Abstract. Video information retrieval. Finding info. relevant to query. Approach. Pseudo-relevance feedback. Negative PRF. Questions. How this paper approach to content-based video retrieval. Relevance Feedback . Relevance Feedback: . Example. Initial Results. Search Engine. 2. Relevance Feedback: . Example. Relevance Feedback. Search Engine. 3. Relevance Feedback: . Example. Revised Results. Information Retrieval in Practice. All slides ©Addison Wesley, 2008. Information Needs. An . information need. is the underlying cause of the query . that a . person submits to a search . engine. sometimes called . . Schütze. and Christina . Lioma. Lecture 9: Relevance Feedback & Query Expansion. 1. 2. Take-. away. . today. Interactive relevance feedback:. improve initial retrieval results by telling the IR system which docs are relevant / . Earth Mover‘s Distance. Marc Wichterich. , Christian Beecks, Martin Sundermeyer, Thomas Seidl. Data Management and Data Exploration Group. RWTH Aachen University, Germany. Introduction. Distance-based Adaptable Similarity Search. sparsity. in web search click data. Qi . Guo. , Dmitry . Lagun. , . Denis Savenkov. , . Qiaoling. Liu. [qguo3. ,dlagun,denis.savenkov,. qiaoling.liu. ]. @. emory.edu. Mathematics . & . Computer . . and. . Interfaces. Information Retrieval in Practice. All slides ©Addison Wesley, 2008. Information Needs. An . information need. is the underlying cause of the query . that a . person submits to a search . Relevance Feedback: . Example. Initial Results. Search Engine. 2. Relevance Feedback: . Example. Relevance Feedback. Search Engine. 3. Relevance Feedback: . Example. Revised Results. Search Engine. 4. for Pseudo–Relevance Feedback . Yuanhua . Lv. . & . ChengXiang. . Zhai. Department of Computer Science, UIUC. Presented by Bo Man . 2014/11/18. Positional Relevance Model . for Pseudo–Relevance Feedback . Keyword Queries. Simple, natural language . queries. . were designed to enable . everyone. to search. Current search engines do . not. perform well (in general) with natural language queries. People trained (in effect) to use keywords. Inspirations, ideas . &. plans. Motivation. Ideal situation: general-purpose image annotation with unlimited vocabulary. Reality:. Classifiers with limited vocabulary and dependency on labeled training data.