-
Munkelt, J.: Erstellung einer DNB-Retrieval-Testkollektion (2018)
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- Pages
- II, 79 S
-
Schultz Jr., W.N.; Braddy, L.: ¬A librarian-centered study of perceptions of subject terms and controlled vocabulary (2017)
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- Source
- Cataloging and classification quarterly. 55(2017) no.7/8, S.456-466
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Colace, F.; Santo, M. de; Greco, L.; Napoletano, P.: Improving relevance feedback-based query expansion by the use of a weighted word pairs approach (2015)
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- Source
- Journal of the Association for Information Science and Technology. 66(2015) no.11, S.2223-2234
-
Li, J.; Zhang, P.; Song, D.; Wu, Y.: Understanding an enriched multidimensional user relevance model by analyzing query logs (2017)
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- Source
- Journal of the Association for Information Science and Technology. 68(2017) no.12, S.2743-2754
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Borlund, P.: ¬A study of the use of simulated work task situations in interactive information retrieval evaluations : a meta-evaluation (2016)
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- Abstract
- Purpose - The purpose of this paper is to report a study of how the test instrument of a simulated work task situation is used in empirical evaluations of interactive information retrieval (IIR) and reported in the research literature. In particular, the author is interested to learn whether the requirements of how to employ simulated work task situations are followed, and whether these requirements call for further highlighting and refinement. Design/methodology/approach - In order to study how simulated work task situations are used, the research literature in question is identified. This is done partly via citation analysis by use of Web of Science®, and partly by systematic search of online repositories. On this basis, 67 individual publications were identified and they constitute the sample of analysis. Findings - The analysis reveals a need for clarifications of how to use simulated work task situations in IIR evaluations. In particular, with respect to the design and creation of realistic simulated work task situations. There is a lack of tailoring of the simulated work task situations to the test participants. Likewise, the requirement to include the test participants' personal information needs is neglected. Further, there is a need to add and emphasise a requirement to depict the used simulated work task situations when reporting the IIR studies. Research limitations/implications - Insight about the use of simulated work task situations has implications for test design of IIR studies and hence the knowledge base generated on the basis of such studies. Originality/value - Simulated work task situations are widely used in IIR studies, and the present study is the first comprehensive study of the intended and unintended use of this test instrument since its introduction in the late 1990's. The paper addresses the need to carefully design and tailor simulated work task situations to suit the test participants in order to obtain the intended authentic and realistic IIR under study.
- Source
- Journal of documentation. 72(2016) no.3, S.394-413
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Kutlu, M.; Elsayed, T.; Lease, M.: Intelligent topic selection for low-cost information retrieval evaluation : a new perspective on deep vs. shallow judging (2018)
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- Abstract
- While test collections provide the cornerstone for Cranfield-based evaluation of information retrieval (IR) systems, it has become practically infeasible to rely on traditional pooling techniques to construct test collections at the scale of today's massive document collections (e.g., ClueWeb12's 700M+ Webpages). This has motivated a flurry of studies proposing more cost-effective yet reliable IR evaluation methods. In this paper, we propose a new intelligent topic selection method which reduces the number of search topics (and thereby costly human relevance judgments) needed for reliable IR evaluation. To rigorously assess our method, we integrate previously disparate lines of research on intelligent topic selection and deep vs. shallow judging (i.e., whether it is more cost-effective to collect many relevance judgments for a few topics or a few judgments for many topics). While prior work on intelligent topic selection has never been evaluated against shallow judging baselines, prior work on deep vs. shallow judging has largely argued for shallowed judging, but assuming random topic selection. We argue that for evaluating any topic selection method, ultimately one must ask whether it is actually useful to select topics, or should one simply perform shallow judging over many topics? In seeking a rigorous answer to this over-arching question, we conduct a comprehensive investigation over a set of relevant factors never previously studied together: 1) method of topic selection; 2) the effect of topic familiarity on human judging speed; and 3) how different topic generation processes (requiring varying human effort) impact (i) budget utilization and (ii) the resultant quality of judgments. Experiments on NIST TREC Robust 2003 and Robust 2004 test collections show that not only can we reliably evaluate IR systems with fewer topics, but also that: 1) when topics are intelligently selected, deep judging is often more cost-effective than shallow judging in evaluation reliability; and 2) topic familiarity and topic generation costs greatly impact the evaluation cost vs. reliability trade-off. Our findings challenge conventional wisdom in showing that deep judging is often preferable to shallow judging when topics are selected intelligently.
- Source
- Information processing and management. 54(2018) no.1, S.37-59
-
Thornley, C.V.; Johnson, A.C.; Smeaton, A.F.; Lee, H.: ¬The scholarly impact of TRECVid (2003-2009) (2011)
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- Source
- Journal of the American Society for Information Science and Technology. 62(2011) no.4, S.613-627
-
Kelly, D.; Sugimoto, C.R.: ¬A systematic review of interactive information retrieval evaluation studies, 1967-2006 (2013)
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- Source
- Journal of the American Society for Information Science and Technology. 64(2013) no.4, S.745-770
-
Ruthven, I.: Relevance behaviour in TREC (2014)
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- Source
- Journal of documentation. 70(2014) no.6, S.1098-1117
-
Tamine, L.; Chouquet, C.; Palmer, T.: Analysis of biomedical and health queries : lessons learned from TREC and CLEF evaluation benchmarks (2015)
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- Source
- Journal of the Association for Information Science and Technology. 66(2015) no.12, S.2626-2642
-
Dang, E.K.F.; Luk, R.W.P.; Allan, J.: ¬A context-dependent relevance model (2016)
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- Source
- Journal of the Association for Information Science and Technology. 67(2016) no.3, S.582-593
-
Günther, M.: Vermitteln Suchmaschinen vollständige Bilder aktueller Themen? : Untersuchung der Gewichtung inhaltlicher Aspekte von Suchmaschinenergebnissen in Deutschland und den USA (2016)
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- Source
- Young information scientists. 1(2016), S.13-29
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Schaer, P.; Mayr, P.; Sünkler, S.; Lewandowski, D.: How relevant is the long tail? : a relevance assessment study on million short (2016)
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-
Behnert, C.; Lewandowski, D.: ¬A framework for designing retrieval effectiveness studies of library information systems using human relevance assessments (2017)
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- Source
- Journal of documentation. 73(2017) no.3, S.509-527
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Sarigil, E.; Sengor Altingovde, I.; Blanco, R.; Barla Cambazoglu, B.; Ozcan, R.; Ulusoy, Ö.: Characterizing, predicting, and handling web search queries that match very few or no results (2018)
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- Source
- Journal of the Association for Information Science and Technology. 69(2018) no.2, S.256-270
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Hider, P.: ¬The search value added by professional indexing to a bibliographic database (2018)
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- Source
- Knowledge organization. 45(2018) no.1, S.23-32
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Losada, D.E.; Parapar, J.; Barreiro, A.: When to stop making relevance judgments? : a study of stopping methods for building information retrieval test collections (2019)
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- Journal of the Association for Information Science and Technology. 70(2019) no.1, S.49-60
-
Angelini, M.; Fazzini, V.; Ferro, N.; Santucci, G.; Silvello, G.: CLAIRE: A combinatorial visual analytics system for information retrieval evaluation (2018)
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- Information processing and management. 54(2018) no.6, S.1077-1100
-
Losada, D.E.; Parapar, J.; Barreiro, A.: Multi-armed bandits for adjudicating documents in pooling-based evaluation of information retrieval systems (2017)
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- Information processing and management. 53(2017) no.5, S.1005-1025
-
White, H.D.: Relevance theory and distributions of judgments in document retrieval (2017)
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- Information processing and management. 53(2017) no.5, S.1080-1102
