Improving Search Effectiveness in the Legal EDiscovery Process Using Relevance Feedback Feng C - Pdf

98K - views

Improving Search Effectiveness in the Legal EDiscovery Process Using Relevance Feedback Feng C

Published bynatalia-silvester

Zhao School of Law University of Washington fczuwashingtonedu Douglas W Oard Coll of Info Stu UMIACS University of Maryland College Park MD 20742 oardumdedu Jason R Baron Of64257ce of the General Counsel National Archives and Records Administration

Tags : Zhao School Law

Embed :

Pdf Download Link

Download Pdf - The PPT/PDF document "Improving Search Effectiveness in the Le..." is the property of its rightful owner. Permission is granted to download and print the materials on this web site for personal, non-commercial use only, and to display it on your personal computer provided you do not modify the materials and that you retain all copyright notices contained in the materials. By downloading content from our website, you accept the terms of this agreement.

Improving Search Effectiveness in the Legal EDiscovery Process Using Relevance Feedback Feng C

Presentation on theme: "Improving Search Effectiveness in the Legal EDiscovery Process Using Relevance Feedback Feng C"— Presentation transcript:

Improving Search Effectiveness in the Legal EDiscovery Process Using Relevance Feedback Feng C - Description

Zhao School of Law University of Washington fczuwashingtonedu Douglas W Oard Coll of Info Stu UMIACS University of Maryland College Park MD 20742 oardumdedu Jason R Baron Of64257ce of the General Counsel National Archives and Records Administration ID: 7425 Download Pdf

Similar presentations

IR Theory: Relevance Feedback

Relevance Feedback: . Example. Initial Results. Search Engine. 2. Relevance Feedback: . Example. Relevance Feedback. Search Engine. 3. Relevance Feedback: . Example. Revised Results. Search Engine. 4.

Ensuring Excellent Feedback

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 .

PSEUDO-RELEVANCE FEEDBACK FOR MULTIMEDIA RETRIEVAL

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.

Positional Relevance Model

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 .

Pseudo-Relevance Feedback For Multimedia Retrieval

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..

Managing Search Relevance

in . SharePoint 2013 and O365. Mikael . Svenson. Principal Consultant. Puzzlepart. SPC382. David . Hollembaek. Senior Consultant. Microsoft. Agenda. The Problem. The Plan. The Tools. The Problem. Is this a good search result?.

Ranking

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.

Improving Search Relevance for Short Queries in

Community Question . Answering. Date. ： . 2014/09/25. Author . ： . Haocheng. Wu, Wei Wu, Ming Zhou, . Enhong. Chen, Lei . Duan. , . Heung-Yeung Shum. Source. ： . WSDM’14. Advisor: .

IR Theory:

Relevance Feedback . Relevance Feedback: . Example. Initial Results. Search Engine. 2. Relevance Feedback: . Example. Relevance Feedback. Search Engine. 3. Relevance Feedback: . Example. Revised Results.

Hinrich

. 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 / .