'Relevance feedback' diaporamas de présentation

User Interfaces for Information Access

User Interfaces for Information Access. Marti Hearst IS202, Fall 2006. Outline. What do people search for? Why is supporting search difficult? What works in search interfaces? When does search result grouping work? What about social tagging and search?.

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Kevin Knight, http://www.isi.edu/natural-language/people/pictures/ieee-expert-1.gif

Kevin Knight, http://www.isi.edu/natural-language/people/pictures/ieee-expert-1.gif. Relevance Feedback Query Expansion. David Kauchak cs160 Fall 2009 adapted from : http://www.stanford.edu/class/cs276/handouts/lecture9-queryexpansion.ppt. Administrative. Assignment 3 out.

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BM25, BM25F, and User Behavior Chris Manning, Pandu Nayak and Prabhakar Raghavan

BM25, BM25F, and User Behavior Chris Manning, Pandu Nayak and Prabhakar Raghavan. Summary - BIM. Boils down to where Simplifies to (with constant p i = 0.5 ). Log odds ratio. Graphical model for BIM. R. Binary variables. tf i. A key limitation of the BIM.

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Query Operations

Query Operations. Relevance Feedback & Query Expansion. Relevance Feedback. After initial retrieval results are presented, allow the user to provide feedback on the relevance of one or more of the retrieved documents. Use this feedback information to reformulate the query.

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Integrating User Feedback Log into Relevance Feedback by Coupled SVM for Content-Based Image Retrieval

Integrating User Feedback Log into Relevance Feedback by Coupled SVM for Content-Based Image Retrieval . 9-April, 2005 Steven C. H. Hoi * , Michael R. Lyu * , Rong Jin # * Department of Computer Science & Engineering The Chinese University of Hong Kong Shatin, N.T., Hong Kong SAR

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Artificial intelligence & natural language processing

Artificial intelligence & natural language processing . Mark Sanderson Porto, 2000. Aims. To provide an outline of the attempts made at using NLP techniques in IR . Objectives. At the end of this lecture you will be able to Outline a range of attempts to get NLP to work with IR systems

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Learning Better Monolingual Models from Bilingual Data

Learning Better Monolingual Models from Bilingual Data. John Blitzer David Burkett, Wei Gao , Dan Klein, Slav Petrov , Ming Zhou. TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box .: A A A A A A A A A A A. Improving Named Entity Recognition (NER). ORG 1.

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Relevance Feedback and Query Expansion

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Georg Buscher , Andreas Dengel, Ludger van Elst German Research Center for AI (DFKI)

Query Expansion Using Gaze-Based Feedback on the Subdocument Level. Georg Buscher , Andreas Dengel, Ludger van Elst German Research Center for AI (DFKI) Knowledge Management Department Kaiserslautern, Germany. SIGIR 08. Outline. Motivation

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Evaluating the Robustness of Learning from Implicit Feedback Filip Radlinski Thorsten Joachims

Evaluating the Robustness of Learning from Implicit Feedback Filip Radlinski Thorsten Joachims. Presentation by Dinesh Bhirud bhiru002@d.umn.edu. Introduction. The paper evaluates the robustness of learning to rank documents based on Implicit feedback. What is implicit feedback?

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reporter: Chen , Yi-wen advisor: Berlin Chen Dept. of Computer Science & Information Engineering

2008 SIGIR. Selecting Good Expansion Terms for Pseudo-Relevance Feedback. Guihong Cao, Jian -Yun Nie , Jianfeng Gao , Stephen Robertson. reporter: Chen , Yi-wen advisor: Berlin Chen Dept. of Computer Science & Information Engineering National Taiwan Normal University.

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Outline

Outline. Latent semantic indexing Dimensionality reduction LSI in information retrieval. Recall: Term- document matrix.

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CVPR2013 Poster

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Initial thoughts about existence in the Web

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Word sense disambiguation and information retrieval

Word sense disambiguation and information retrieval. Chapter 17 Jurafsky, D. & Martin J. H. SPEECH and LANGUAGE PROCESSING Jarmo Ritola - jarmo.ritola@hut.fi. Lexical Semantic Processing. Word sense disambiguation which sense of a word is being used non-trivial task robust algorithms

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Lecture 2: Retrieval Models

Lecture 2: Retrieval Models. Maya Ramanath. QQ1. Vector space model: 0 for non-presence of a term, 1 for presence: Query: q1 AND q2 AND q3 Compare the set of results returned by the vector space model and boolean model. Term weighting (1/3): tf. Query: Cat. D2. D3. D1. D1. D3.

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Introduction

Introduction. Query operations attempt to reformulate the original query in order to enhance the efficacy of IR Query operations typically involve Expanding the original query Reweighing the terms in the expanded query

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Deliverable #3: Document and Passage Retrieval

Deliverable #3: Document and Passage Retrieval. Ling 573 NLP Systems and Applications May 10, 2011. Main Components. Document retrieval Evaluated with Mean Average Precision Passage retrieval/re-ranking Evaluated with Mean Reciprocal Rank (MRR). Mean Average Precision (MAP).

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