Results 1 - 10 of 2,364

On Expanding Query Vectors with Lexically Related Words

by Ellen M. Voorhees , 1994
"... Experiments performed on small collections suggest that expanding query vectors with words that are lexically related to the original query words can improve retrieval effectiveness. Prior experiments using WordNet to automatically expand vectors in the large TREC-1 collection were inconclusive rega ..."
Abstract - Cited by 36 (2 self) - Add to MetaCart
Experiments performed on small collections suggest that expanding query vectors with words that are lexically related to the original query words can improve retrieval effectiveness. Prior experiments using WordNet to automatically expand vectors in the large TREC-1 collection were inconclusive

Support vector machine active learning for image retrieval

by Simon Tong , 2001
"... Relevance feedback is often a critical component when designing image databases. With these databases it is difficult to specify queries directly and explicitly. Relevance feedback interactively determinines a user’s desired output or query concept by asking the user whether certain proposed images ..."
Abstract - Cited by 456 (28 self) - Add to MetaCart
are relevant or not. For a relevance feedback algorithm to be effective, it must grasp a user’s query concept accurately and quickly, while also only asking the user to label a small number of images. We propose the use of a support vector machine active learning algorithm for conducting effective relevance

Topic-Sensitive PageRank

by Taher Haveliwala , 2002
"... In the original PageRank algorithm for improving the ranking of search-query results, a single PageRank vector is computed, using the link structure of the Web, to capture the relative "importance" of Web pages, independent of any particular search query. To yield more accurate search resu ..."
Abstract - Cited by 543 (10 self) - Add to MetaCart
In the original PageRank algorithm for improving the ranking of search-query results, a single PageRank vector is computed, using the link structure of the Web, to capture the relative "importance" of Web pages, independent of any particular search query. To yield more accurate search

Query expansion using lexical-semantic relations

by Ellen M. Voorhees - In Proceedings of the 17th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval , 1994
"... Applications such as office automation, news filtering, help facilities in complex systems, and the like require the ability to retrieve documents from full-text databases where vocabulary problems can be particularly severe. Experiments performed on small collections with single-domain thesauri sug ..."
Abstract - Cited by 395 (1 self) - Add to MetaCart
suggest that expanding query vectors with words that are lexically related to the original query words can ameliorate some of the problems of mismatched vocabularies. This paper examines the utility of lexical query expansion in the large, diverse TREC collection. Concepts are represented by Word

The SR-tree: An Index Structure for High-Dimensional Nearest Neighbor Queries

by Norio Katayama, et al. , 1997
"... Recently, similarity queries on feature vectors have been widely used to perform content-based retrieval of images. To apply this technique to large databases, it is required to develop multidimensional index structures supporting nearest neighbor queries e ciently. The SS-tree had been proposed for ..."
Abstract - Cited by 438 (3 self) - Add to MetaCart
Recently, similarity queries on feature vectors have been widely used to perform content-based retrieval of images. To apply this technique to large databases, it is required to develop multidimensional index structures supporting nearest neighbor queries e ciently. The SS-tree had been proposed

Indexing by latent semantic analysis

by Scott Deerwester, Susan T. Dumais, George W. Furnas, Thomas K. Landauer, Richard Harshman - JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE , 1990
"... A new method for automatic indexing and retrieval is described. The approach is to take advantage of implicit higher-order structure in the association of terms with documents (“semantic structure”) in order to improve the detection of relevant documents on the basis of terms found in queries. The p ..."
Abstract - Cited by 3779 (35 self) - Add to MetaCart
. The particular technique used is singular-value decomposition, in which a large term by document matrix is decomposed into a set of ca. 100 or-thogonal factors from which the original matrix can be approximated by linear combination. Documents are represented by ca. 100 item vectors of factor weights. Queries

Optimizing Search Engines using Clickthrough Data

by Thorsten Joachims , 2002
"... This paper presents an approach to automatically optimizing the retrieval quality of search engines using clickthrough data. Intuitively, a good information retrieval system should present relevant documents high in the ranking, with less relevant documents following below. While previous approaches ..."
Abstract - Cited by 1314 (23 self) - Add to MetaCart
approaches to learning retrieval functions from examples exist, they typically require training data generated from relevance judgments by experts. This makes them difficult and expensive to apply. The goal of this paper is to develop a method that utilizes clickthrough data for training, namely the query

Using Linear Algebra for Intelligent Information Retrieval

by Michael W. Berry, Susan T. Dumais - SIAM REVIEW , 1995
"... Currently, most approaches to retrieving textual materials from scientific databases depend on a lexical match between words in users' requests and those in or assigned to documents in a database. Because of the tremendous diversity in the words people use to describe the same document, lexical ..."
Abstract - Cited by 676 (18 self) - Add to MetaCart
by 200-300 of the largest singular vectors are then matched against user queries. We call this retrieval method Latent Semantic Indexing (LSI) because the subspace represents important associative relationships between terms and documents that are not evident in individual documents. LSI is a completely

M-tree: An Efficient Access Method for Similarity Search in Metric Spaces

by Paolo Ciaccia, Marco Patella, Pavel Zezula , 1997
"... A new access meth d, called M-tree, is proposed to organize and search large data sets from a generic "metric space", i.e. whE4 object proximity is only defined by a distance function satisfyingth positivity, symmetry, and triangle inequality postulates. We detail algorith[ for insertion o ..."
Abstract - Cited by 663 (38 self) - Add to MetaCart
of objects and split management, whF h keep th M-tree always balanced - severalheralvFV split alternatives are considered and experimentally evaluated. Algorithd for similarity (range and k-nearest neigh bors) queries are also described. Results from extensive experimentationwith a prototype system

Searching in metric spaces

by Edgar Chávez, Gonzalo Navarro, Ricardo Baeza-yates, José Luis Marroquín , 2001
"... The problem of searching the elements of a set that are close to a given query element under some similarity criterion has a vast number of applications in many branches of computer science, from pattern recognition to textual and multimedia information retrieval. We are interested in the rather gen ..."
Abstract - Cited by 436 (38 self) - Add to MetaCart
The problem of searching the elements of a set that are close to a given query element under some similarity criterion has a vast number of applications in many branches of computer science, from pattern recognition to textual and multimedia information retrieval. We are interested in the rather
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