Search (51 results, page 1 of 3)

Shiri, A.A.; Revie, C.: Query expansion behavior within a thesaurus-enhanced search environment : a user-centered evaluation (2006) 0.01
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Abstract

The study reported here investigated the query expansion behavior of end-users interacting with a thesaurus-enhanced search system on the Web. Two groups, namely academic staff and postgraduate students, were recruited into this study. Data were collected from 90 searches performed by 30 users using the OVID interface to the CAB abstracts database. Data-gathering techniques included questionnaires, screen capturing software, and interviews. The results presented here relate to issues of search-topic and search-term characteristics, number and types of expanded queries, usefulness of thesaurus terms, and behavioral differences between academic staff and postgraduate students in their interaction. The key conclusions drawn were that (a) academic staff chose more narrow and synonymous terms than did postgraduate students, who generally selected broader and related terms; (b) topic complexity affected users' interaction with the thesaurus in that complex topics required more query expansion and search term selection; (c) users' prior topic-search experience appeared to have a significant effect on their selection and evaluation of thesaurus terms; (d) in 50% of the searches where additional terms were suggested from the thesaurus, users stated that they had not been aware of the terms at the beginning of the search; this observation was particularly noticeable in the case of postgraduate students.

Date

22. 7.2006 16:32:43

Ziegler, B.: ESS: ein schneller Algorithmus zur Mustersuche in Zeichenfolgen (1996) 0.01

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Silveira, M.; Ribeiro-Neto, B.: Concept-based ranking : a case study in the juridical domain (2004) 0.01

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Chang, R.: ¬The development of indexing technology (1993) 0.01

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Abstract: Reviews the basic techniques of computerized indexing, including various file accessing methods such as: Sequential Access Method (SAM); Direct Access Method (DAM); Indexed Sequential Access Method (ISAM), and Virtual Indexed Sequential Access Method (VSAM); and various B-tree (balanced tree)structures. Illustrates how records are stored and accessed, and how B-trees are used to for improving the operations of information retrieval and maintenance

Chang, R.: Keyword searching and indexing (1993) 0.01

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Abstract: Explains how a computer indexing system works. Reviews fundamentals of how data are stored and retrieved by computers. Describes B-Tree and B+-Tree indexing structures. Gives basic keyword searching techniques that the user must apply to make use of the indexing programs. The demand for keyword retrieval is increasing and librarians should expect to see the keyword-indexing feature become commonly available

Quint, B.: Check out the new RANK command on DIALOG (1993) 0.01

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Karlsson, A.; Hammarfelt, B.; Steinhauer, H.J.; Falkman, G.; Olson, N.; Nelhans, G.; Nolin, J.: Modeling uncertainty in bibliometrics and information retrieval : an information fusion approach (2015) 0.01

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Moura, E.S. de; Fernandes, D.; Ribeiro-Neto, B.; Silva, A.S. da; Gonçalves, M.A.: Using structural information to improve search in Web collections (2010) 0.01
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Abstract

In this work, we investigate the problem of using the block structure of Web pages to improve ranking results. Starting with basic intuitions provided by the concepts of term frequency (TF) and inverse document frequency (IDF), we propose nine block-weight functions to distinguish the impact of term occurrences inside page blocks, instead of inside whole pages. These are then used to compute a modified BM25 ranking function. Using four distinct Web collections, we ran extensive experiments to compare our block-weight ranking formulas with two other baselines: (a) a BM25 ranking applied to full pages, and (b) a BM25 ranking that takes into account best blocks. Our methods suggest that our block-weighting ranking method is superior to all baselines across all collections we used and that average gain in precision figures from 5 to 20% are generated.

Voorhees, E.M.: Implementing agglomerative hierarchic clustering algorithms for use in document retrieval (1986) 0.01

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Source: Information processing and management. 22(1986) no.6, S.465-476

Shah, B.; Raghavan, V.; Dhatric, P.; Zhao, X.: ¬A cluster-based approach for efficient content-based image retrieval using a similarity-preserving space transformation method (2006) 0.01
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Abstract

The techniques of clustering and space transformation have been successfully used in the past to solve a number of pattern recognition problems. In this article, the authors propose a new approach to content-based image retrieval (CBIR) that uses (a) a newly proposed similarity-preserving space transformation method to transform the original low-level image space into a highlevel vector space that enables efficient query processing, and (b) a clustering scheme that further improves the efficiency of our retrieval system. This combination is unique and the resulting system provides synergistic advantages of using both clustering and space transformation. The proposed space transformation method is shown to preserve the order of the distances in the transformed feature space. This strategy makes this approach to retrieval generic as it can be applied to object types, other than images, and feature spaces more general than metric spaces. The CBIR approach uses the inexpensive "estimated" distance in the transformed space, as opposed to the computationally inefficient "real" distance in the original space, to retrieve the desired results for a given query image. The authors also provide a theoretical analysis of the complexity of their CBIR approach when used for color-based retrieval, which shows that it is computationally more efficient than other comparable approaches. An extensive set of experiments to test the efficiency and effectiveness of the proposed approach has been performed. The results show that the approach offers superior response time (improvement of 1-2 orders of magnitude compared to retrieval approaches that either use pruning techniques like indexing, clustering, etc., or space transformation, but not both) with sufficiently high retrieval accuracy.
Dang, E.K.F.; Luk, R.W.P.; Allan, J.; Ho, K.S.; Chung, K.F.L.; Lee, D.L.: ¬A new context-dependent term weight computed by boost and discount using relevance information (2010) 0.01
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Abstract

We studied the effectiveness of a new class of context-dependent term weights for information retrieval. Unlike the traditional term frequency-inverse document frequency (TF-IDF), the new weighting of a term t in a document d depends not only on the occurrence statistics of t alone but also on the terms found within a text window (or "document-context") centered on t. We introduce a Boost and Discount (B&D) procedure which utilizes partial relevance information to compute the context-dependent term weights of query terms according to a logistic regression model. We investigate the effectiveness of the new term weights compared with the context-independent BM25 weights in the setting of relevance feedback. We performed experiments with title queries of the TREC-6, -7, -8, and 2005 collections, comparing the residual Mean Average Precision (MAP) measures obtained using B&D term weights and those obtained by a baseline using BM25 weights. Given either 10 or 20 relevance judgments of the top retrieved documents, using the new term weights yields improvement over the baseline for all collections tested. The MAP obtained with the new weights has relative improvement over the baseline by 3.3 to 15.2%, with statistical significance at the 95% confidence level across all four collections.

Jacucci, G.; Barral, O.; Daee, P.; Wenzel, M.; Serim, B.; Ruotsalo, T.; Pluchino, P.; Freeman, J.; Gamberini, L.; Kaski, S.; Blankertz, B.: Integrating neurophysiologic relevance feedback in intent modeling for information retrieval (2019) 0.01

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Dang, E.K.F.; Luk, R.W.P.; Allan, J.: ¬A retrieval model family based on the probability ranking principle for ad hoc retrieval (2022) 0.01
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Abstract

Many successful retrieval models are derived based on or conform to the probability ranking principle (PRP). We present a new derivation of a document ranking function given by the probability of relevance of a document, conforming to the PRP. Our formulation yields a family of retrieval models, called probabilistic binary relevance (PBR) models, with various instantiations obtained by different probability estimations. By extensive experiments on a range of TREC collections, improvement of the PBR models over some established baselines with statistical significance is observed, especially in the large Clueweb09 Cat-B collection.

Smeaton, A.F.; Rijsbergen, C.J. van: ¬The retrieval effects of query expansion on a feedback document retrieval system (1983) 0.01

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Date: 30. 3.2001 13:32:22

Back, J.: ¬An evaluation of relevancy ranking techniques used by Internet search engines (2000) 0.01

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Date: 25. 8.2005 17:42:22

Zhu, B.; Chen, H.: Validating a geographical image retrieval system (2000) 0.01

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Drucker, H.; Shahrary, B.; Gibbon, D.C.: Support vector machines : relevance feedback and information retrieval (2002) 0.01

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Dominich, S.; Skrop, A.: PageRank and interaction information retrieval (2005) 0.01
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Abstract

The PageRank method is used by the Google Web search engine to compute the importance of Web pages. Two different views have been developed for the Interpretation of the PageRank method and values: (a) stochastic (random surfer): the PageRank values can be conceived as the steady-state distribution of a Markov chain, and (b) algebraic: the PageRank values form the eigenvector corresponding to eigenvalue 1 of the Web link matrix. The Interaction Information Retrieval (1**2 R) method is a nonclassical information retrieval paradigm, which represents a connectionist approach based an dynamic systems. In the present paper, a different Interpretation of PageRank is proposed, namely, a dynamic systems viewpoint, by showing that the PageRank method can be formally interpreted as a particular case of the Interaction Information Retrieval method; and thus, the PageRank values may be interpreted as neutral equilibrium points of the Web.

Lin, J.; Katz, B.: Building a reusable test collection for question answering (2006) 0.01

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Cecchini, R.L.; Lorenzetti, C.M.; Maguitman, A.G.; Brignole, N.B.: Multiobjective evolutionary algorithms for context-based search (2010) 0.01
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Abstract

Formulating high-quality queries is a key aspect of context-based search. However, determining the effectiveness of a query is challenging because multiple objectives, such as high precision and high recall, are usually involved. In this work, we study techniques that can be applied to evolve contextualized queries when the criteria for determining query quality are based on multiple objectives. We report on the results of three different strategies for evolving queries: (a) single-objective, (b) multiobjective with Pareto-based ranking, and (c) multiobjective with aggregative ranking. After a comprehensive evaluation with a large set of topics, we discuss the limitations of the single-objective approach and observe that both the Pareto-based and aggregative strategies are highly effective for evolving topical queries. In particular, our experiments lead us to conclude that the multiobjective techniques are superior to a baseline as well as to well-known and ad hoc query reformulation techniques.

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