We think you have liked this presentation. If you wish to download it, please recommend it to your friends in any social system. Share buttons are a little bit lower. Thank you!
Presentation is loading. Please wait.
Published byLeroy Jenison
Modified over 2 years ago
Information Access and the User Experience Daniel Tunkelang Chief Scientist, Endeca SIGIR 2007 – Industry Day
Copyright©2007 Endeca Technologies, Inc. All rights reserved. Proprietary and confidential. Overview Endeca Information Access and User Experience
Copyright©2007 Endeca Technologies, Inc. All rights reserved. Proprietary and confidential. Endeca Founded in 1999. Headquartered in Cambridge, Massachusetts. 450+ employees in 2007! Leading provider of enterprise information access.
Copyright©2007 Endeca Technologies, Inc. All rights reserved. Proprietary and confidential. Endeca’s Customers
Copyright©2007 Endeca Technologies, Inc. All rights reserved. Proprietary and confidential. Information Retrieval: System-Centered User input is a fixed query string. Optimized for precision, recall.
Copyright©2007 Endeca Technologies, Inc. All rights reserved. Proprietary and confidential. Information Access: User-Centered User interacts with system. Optimized for communication.
Copyright©2007 Endeca Technologies, Inc. All rights reserved. Proprietary and confidential. Information Access = Summarization Communicating with the user = showing more than the top ranked documents!
Copyright©2007 Endeca Technologies, Inc. All rights reserved. Proprietary and confidential. Information Access -> User Experience
Copyright©2007 Endeca Technologies, Inc. All rights reserved. Proprietary and confidential. Faceted Search and Navigation
Copyright©2007 Endeca Technologies, Inc. All rights reserved. Proprietary and confidential. Query Expansion and Clustering A search for apple production:
Copyright©2007 Endeca Technologies, Inc. All rights reserved. Proprietary and confidential. Social Search
Copyright©2007 Endeca Technologies, Inc. All rights reserved. Proprietary and confidential. Text Analytics U.S. News about Iraq: Weapons Inspectors Abu Ghraib 1 st Iraq Invasion September 11 2 nd Iraq Invasion
Copyright©2007 Endeca Technologies, Inc. All rights reserved. Proprietary and confidential. Conclusion Information Access = User-Centered IR Summarization Enables Dialog
© 2008 Endeca Technologies, Inc. All rights reserved. Guided Summarization Daniel Tunkelang Chief Scientist, Endeca Industry Day.
© 2009 Endeca Technologies, Inc. All rights reserved. exploring semantic means Daniel Tunkelang Chief Scientist, Endeca.
© 2008 Endeca Technologies, Inc. All rights reserved. Is Search Broken?! Daniel Tunkelang Chief Scientist, Endeca.
Challenges in Information Retrieval and Language Modeling Michael Shepherd Dalhousie University Halifax, NS Canada.
WHIM- Spring ‘10 By:-Enza Desai. What is HCIR? Study of IR techniques that brings human intelligence into search process. Coined by Gary Marchionini.
The Loquacious ( 愛說話 ) User: A Document-Independent Source of Terms for Query Expansion Diane Kelly et al. University of North Carolina at Chapel Hill.
Clustering Top-Ranking Sentences for Information Access Anastasios Tombros, Joemon Jose, Ian Ruthven University of Glasgow & University of Strathclyde.
© 1990—2006 Visual Knowledge Software® | Private and Confidential | 2 Semantic Agent Wikis For Engineering.
Advantages of Query Biased Summaries in Information Retrieval by A. Tombros and M. Sanderson Presenters: Omer Erdil Albayrak Bilge Koroglu.
Information retrieval Finding relevant data using irrelevant keys Example: database of photographic images sorted by number, date. DBMS: Well structured.
DCMI and Hierarchical Faceted Metadata Navigation.
Proposal for Term Project J. H. Wang Mar. 2, 2015.
Copyright © 2015 Pearson Education, Inc. or its affiliates. All rights reserved District Assessment Coordinators Annual Meeting September 9, 2015.
© Experian Information Solutions, Inc All rights reserved. Experian and the marks used herein are service marks or registered trademarks of Experian.
Good MDM IOS Overview Presented by: Jerry Wen 02/09/2012.
Re-ranking Documents Segments To Improve Access To Relevant Content in Information Retrieval Gary Madden Applied Computational Linguistics Dublin City.
Query Suggestions in the Absence of Query Logs Sumit Bhatia, Debapriyo Majumdar,Prasenjit Mitra SIGIR’11, July 24–28, 2011, Beijing, China.
Michael Bendersky, W. Bruce Croft Dept. of Computer Science Univ. of Massachusetts Amherst Amherst, MA SIGIR
Search is not only about the Web An Overview on Printed Documents Search and Patent Search Walid Magdy Centre for Next Generation Localisation School of.
Knowledge and Learning in Complex Business Systems Zuobing Xu University of California, Santa Cruz (Silicon Valley Center) Ram Akella, Kristin Fridgeirsdottir,
Modern Information Retrieval: A Brief Overview By Amit Singhal Ranjan Dash.
© Experian Information Solutions, Inc All rights reserved. Confidential and proprietary. Press ALT+F4 to quit Take a risk-based approach to authentication.
CS598CXZ Course Summary ChengXiang Zhai Department of Computer Science University of Illinois, Urbana-Champaign.
CONVERSION ARCHITECTURE CONVERSION ARCHITECTURE Testing data Keyword expansion Historical data Conversion analysis Geographic data Keyword analysis Visual.
Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide
Chapter 14 An Overview of Query Optimization. Copyright © 2005 Pearson Addison-Wesley. All rights reserved Figure 14.1 Typical architecture for.
Question Answering using Language Modeling Some workshop-level thoughts.
A machine learning approach to improve precision for navigational queries in a Web information retrieval system Reiner Kraft
Query Expansion By: Sean McGettrick. What is Query Expansion? Query Expansion is the term given when a search engine adding search terms to a user’s weighted.
Advanced Semantics and Search Beyond Tag Clouds and Taxonomies Tom Reamy Chief Knowledge Architect KAPS Group Knowledge Architecture Professional Services.
Survey on Long Queries in Keyword Search : Phrase-based IR Sungchan Park
Document Clustering for Natural Language Dialogue-based IR (Google for the Blind) Antoine Raux IR Seminar and Lab Fall 2003 Initial Presentation.
Social Commerce: Disruptive Potential of Social Media in Ecommerce and Classifieds Thad Eby, UK Director.
1 Query Operations Relevance Feedback & Query Expansion.
Less is More Probabilistic Models for Retrieving Fewer Relevant Documents Harr Chen, David R. Karger MIT CSAIL ACM SIGIR 2006 August 9, 2006.
With or without users? Julio Gonzalo UNEDhttp://nlp.uned.es.
University Of Seoul Ubiquitous Sensor Network Lab Query Dependent Pseudo-Relevance Feedback based on Wikipedia 전자전기컴퓨터공학 부 USN 연구실 G
Sam Mefford Enterprise Search Practice Lead Avalon Consulting, LLC. Changing ‘Hide and Seek’ to ‘Index and Find’
TextMOLE: Text Mining Operations Library and Environment Daniel B. Waegel and April Kontostathis, Ph.D. Ursinus College Collegeville PA.
1 Personalizing Search via Automated Analysis of Interests and Activities Jaime Teevan, MIT Susan T. Dumais, Microsoft Eric Horvitz, Microsoft SIGIR 2005.
Metadata in Carrot II Current metadata –TF.IDF for both documents and collections –Full-text index –Metadata are transferred between different nodes Potential.
Search Result Interface Hongning Wang Abstraction of search engine architecture User Ranker Indexer Doc Analyzer Index results Crawler Doc Representation.
Information Retrieval Visualization CPSC 533c Class Presentation Qixing Zheng March 22, 2004.
Modern Information Retrieval Lecture 2: Key concepts in IR.
MAKING SHAREPOINT® AND FAST® SEARCH UNIVERSAL AND ACTIONABLE BA Insight Introduction and Overview.
1 Copyright © 2013, AAXIS Commerce. All rights reserved. Confidential. Recent AAXIS Commerce Success Stories.
Measuring How Good Your Search Engine Is. *. Information System Evaluation l Before 1993 evaluations were done using a few small, well-known corpora of.
Major Issues n Information is mostly online n Information is increasing available in full-text (full-content) n There is an explosion in the amount of.
Acceso a la información mediante exploración de sintagmas Anselmo Peñas, Julio Gonzalo y Felisa Verdejo Dpto. Lenguajes y Sistemas Informáticos UNED III.
Semantic (Language) Models: Robustness, Structure & Beyond Thomas Hofmann Department of Computer Science Brown University Chief Scientist.
© 2017 SlidePlayer.com Inc. All rights reserved.