Find results with:

Add another field

Search by date

to

Searching collections:

Dartmouth Dissertations

Add or remove collections

Home Dartmouth Dissertations Page 1

Reference URL

To link to the entire object, paste this link in email, IM or document

To embed the entire object, paste this HTML in website

To link to this page, paste this link in email, IM or document

To embed this page, paste this HTML in website

On-line metasearch, pooling and system evaluation.

you wish to report:

Your comment:

Your Name:

Submit

Cancel

...

Back to top

Powered by CONTENTdm®

Call No.	Ph.D. 2005
Author	Savell, Robert A.
Title	On-line metasearch, pooling and system evaluation.
Date	2005
Abstract	This thesis presents a unified method for simultaneous solution of three problems in Information Retrieval--metasearch (the fusion of ranked lists returned by retrieval systems to elicit improved performance), efficient system evaluation (the accurate evaluation of retrieval systems with small numbers of relevance judgements), and pooling or "active sample selection" (the selection of documents for manual judgement, in order to develop sample pools of high precision or pools suitable for assessing system quality). The thesis establishes a unified theoretical framework for addressing these three problems and naturally generalizes their solution to the on-line context by incorporating feedback in the form of relevance judgements. The algorithm-- Rankhedge for on-line retrieval, metasearch and system evaluation--is the first to address these three problems simultaneously and also to generalize their solution to the on-line context. Optimality of the Rankhedge algorithm is developed via Bayesian and maximum entropy interpretations. Results of the algorithm prove to be significantly superior to previous methods when tested over a range of TREC (Text REtrieval Conference) data. In the absence of feedback, the technique equals or exceeds the performance of benchmark metasearch algorithms such as CombMNZ and Condorcet. The technique then dramatically improves on this performance during the on-line metasearch process. In addition, the technique generates pools of documents which include more relevant documents and produce more accurate system evaluations than previous techniques. The thesis includes an information-theoretic examination of the original Hedge algorithm as well as its adaptation to the context of ranked lists. The work also addresses the concept of information-theoretic similarity within the Rankhedge context and presents a method for decorrelating the predictor set to improve worst case performance. Finally, an information-theoretically optimal method for probabilistic "active sampling" is presented with possible application to a broad range of practical and theoretical contexts.
Description	xii, 172 leaves : ill. 28 cm.
Format	application/pdf
Advisor	Daniel Rockmore.
Subject	Information retrieval -- Mathematical models. ; Electronic information resource searching. ; Computer algorithms. ; Decision making -- Data processing. ; Mathematical optimization.
College	Dartmouth College -- Department of Computer Science.
Issuing Body	Dartmouth Dissertations are made available through the Dartmouth Digital Library.
Degree	Thesis (Ph.D.)--Dartmouth College.
Copyright	All rights reserved. No part of this book may be reproduced in any form by any electronic or mechanical means without permission in writing from the author.
DOI	http://dx.doi.org/10.1349/ddlp.1683

	All
	Pages Enter page numbers or page ranges separated by commas. For example: 1,3,5-12

Find results with:

Searching collections:

Home Dartmouth Dissertations Page 1

On-line metasearch, pooling and system evaluation.

Page 1

Object Description

Description

On-line metasearch, pooling and system evaluation.

On-line metasearch, pooling and system evaluation.