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

Author : erica | Published Date : 2024-07-08

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

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

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 . for 2015 Baldrige Award Applicants . “Straight A” Feedback Comments. A. ctionable. . A. ccurate . A. dequate. A. ligned*. *. Added Emphasis in 2015. Actionable:. . relevant to the applicant’s key factors and specific enough that the organization can use the feedback to sustain or improve its . 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 . Clickthroughs. for News Search. Hongning. Wang. +. , . Anlei. Dong. *. , . Lihong. Li. *. , Yi Chang. *. , . Evgeniy. . Gabrilovich. *. +. CS@UIUC . *. Yahoo! Labs. Relevance . v.s. . Freshness. 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 : . 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.. Alicia Wood. What is the . problem. to be solved?. Problem. I. mperfect description of need. Search engine not able to retrieve documents matching query . N. eed accurate and related query substitutions. . 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. Alexander . Kotov. . and . ChengXiang. . Zhai. . University of Illinois at Urbana-Champaign. Roadmap. Query Ambiguity. Interactive Sense Feedback. Experiments. Upper-bound performance. User study. Jaime Teevan, Susan Dumais, Dan Liebling. Microsoft Research. “grand . copthorne. waterfront”. “. singapore. ”. How Do the Two Queries Differ?. grand . copthorne. waterfront. v. . singapore. David Collings (ECU) and Bruce Guthrie (GCA. ). In this session: . Supplementing the UES. Why workplace relevance?. WRS Development. Source, versions, items. Workplace Relevance Scale. Dennis . Trewen. 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 . 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.

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