-
Khoo, C.S.G.; Wan, K.-W.: ¬A simple relevancy-ranking strategy for an interface to Boolean OPACs (2004)
0.05
0.045311455 = product of:
0.08156061 = sum of:
0.015000606 = product of:
0.030001212 = sum of:
0.030001212 = weight(_text_:headings in 2509) [ClassicSimilarity], result of:
0.030001212 = score(doc=2509,freq=2.0), product of:
0.15996648 = queryWeight, product of:
4.849944 = idf(docFreq=940, maxDocs=44218)
0.03298316 = queryNorm
0.18754686 = fieldWeight in 2509, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
4.849944 = idf(docFreq=940, maxDocs=44218)
0.02734375 = fieldNorm(doc=2509)
0.5 = coord(1/2)
0.01763606 = weight(_text_:library in 2509) [ClassicSimilarity], result of:
0.01763606 = score(doc=2509,freq=8.0), product of:
0.08672522 = queryWeight, product of:
2.6293786 = idf(docFreq=8668, maxDocs=44218)
0.03298316 = queryNorm
0.20335563 = fieldWeight in 2509, product of:
2.828427 = tf(freq=8.0), with freq of:
8.0 = termFreq=8.0
2.6293786 = idf(docFreq=8668, maxDocs=44218)
0.02734375 = fieldNorm(doc=2509)
0.012079521 = weight(_text_:of in 2509) [ClassicSimilarity], result of:
0.012079521 = score(doc=2509,freq=30.0), product of:
0.05157766 = queryWeight, product of:
1.5637573 = idf(docFreq=25162, maxDocs=44218)
0.03298316 = queryNorm
0.23420064 = fieldWeight in 2509, product of:
5.477226 = tf(freq=30.0), with freq of:
30.0 = termFreq=30.0
1.5637573 = idf(docFreq=25162, maxDocs=44218)
0.02734375 = fieldNorm(doc=2509)
0.02902409 = weight(_text_:congress in 2509) [ClassicSimilarity], result of:
0.02902409 = score(doc=2509,freq=2.0), product of:
0.15733992 = queryWeight, product of:
4.7703104 = idf(docFreq=1018, maxDocs=44218)
0.03298316 = queryNorm
0.18446743 = fieldWeight in 2509, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
4.7703104 = idf(docFreq=1018, maxDocs=44218)
0.02734375 = fieldNorm(doc=2509)
0.007820335 = product of:
0.01564067 = sum of:
0.01564067 = weight(_text_:22 in 2509) [ClassicSimilarity], result of:
0.01564067 = score(doc=2509,freq=2.0), product of:
0.11550141 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.03298316 = queryNorm
0.1354154 = fieldWeight in 2509, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.02734375 = fieldNorm(doc=2509)
0.5 = coord(1/2)
0.5555556 = coord(5/9)
- Abstract
- A relevancy-ranking algorithm for a natural language interface to Boolean online public access catalogs (OPACs) was formulated and compared with that currently used in a knowledge-based search interface called the E-Referencer, being developed by the authors. The algorithm makes use of seven weIl-known ranking criteria: breadth of match, section weighting, proximity of query words, variant word forms (stemming), document frequency, term frequency and document length. The algorithm converts a natural language query into a series of increasingly broader Boolean search statements. In a small experiment with ten subjects in which the algorithm was simulated by hand, the algorithm obtained good results with a mean overall precision of 0.42 and mean average precision of 0.62, representing a 27 percent improvement in precision and 41 percent improvement in average precision compared to the E-Referencer. The usefulness of each step in the algorithm was analyzed and suggestions are made for improving the algorithm.
- Content
- "Most Web search engines accept natural language queries, perform some kind of fuzzy matching and produce ranked output, displaying first the documents that are most likely to be relevant. On the other hand, most library online public access catalogs (OPACs) an the Web are still Boolean retrieval systems that perform exact matching, and require users to express their search requests precisely in a Boolean search language and to refine their search statements to improve the search results. It is well-documented that users have difficulty searching Boolean OPACs effectively (e.g. Borgman, 1996; Ensor, 1992; Wallace, 1993). One approach to making OPACs easier to use is to develop a natural language search interface that acts as a middleware between the user's Web browser and the OPAC system. The search interface can accept a natural language query from the user and reformulate it as a series of Boolean search statements that are then submitted to the OPAC. The records retrieved by the OPAC are ranked by the search interface before forwarding them to the user's Web browser. The user, then, does not need to interact directly with the Boolean OPAC but with the natural language search interface or search intermediary. The search interface interacts with the OPAC system an the user's behalf. The advantage of this approach is that no modification to the OPAC or library system is required. Furthermore, the search interface can access multiple OPACs, acting as a meta search engine, and integrate search results from various OPACs before sending them to the user. The search interface needs to incorporate a method for converting the user's natural language query into a series of Boolean search statements, and for ranking the OPAC records retrieved. The purpose of this study was to develop a relevancyranking algorithm for a search interface to Boolean OPAC systems. This is part of an on-going effort to develop a knowledge-based search interface to OPACs called the E-Referencer (Khoo et al., 1998, 1999; Poo et al., 2000). E-Referencer v. 2 that has been implemented applies a repertoire of initial search strategies and reformulation strategies to retrieve records from OPACs using the Z39.50 protocol, and also assists users in mapping query keywords to the Library of Congress subject headings."
- Source
- Electronic library. 22(2004) no.2, S.112-120
-
Back, J.: ¬An evaluation of relevancy ranking techniques used by Internet search engines (2000)
0.03
0.026343048 = product of:
0.07902914 = sum of:
0.03527212 = weight(_text_:library in 3445) [ClassicSimilarity], result of:
0.03527212 = score(doc=3445,freq=2.0), product of:
0.08672522 = queryWeight, product of:
2.6293786 = idf(docFreq=8668, maxDocs=44218)
0.03298316 = queryNorm
0.40671125 = fieldWeight in 3445, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
2.6293786 = idf(docFreq=8668, maxDocs=44218)
0.109375 = fieldNorm(doc=3445)
0.012475675 = weight(_text_:of in 3445) [ClassicSimilarity], result of:
0.012475675 = score(doc=3445,freq=2.0), product of:
0.05157766 = queryWeight, product of:
1.5637573 = idf(docFreq=25162, maxDocs=44218)
0.03298316 = queryNorm
0.24188137 = fieldWeight in 3445, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
1.5637573 = idf(docFreq=25162, maxDocs=44218)
0.109375 = fieldNorm(doc=3445)
0.03128134 = product of:
0.06256268 = sum of:
0.06256268 = weight(_text_:22 in 3445) [ClassicSimilarity], result of:
0.06256268 = score(doc=3445,freq=2.0), product of:
0.11550141 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.03298316 = queryNorm
0.5416616 = fieldWeight in 3445, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.109375 = fieldNorm(doc=3445)
0.5 = coord(1/2)
0.33333334 = coord(3/9)
- Date
- 25. 8.2005 17:42:22
- Source
- Library and information research news. 24(2000) no.77, S.30-34
-
Nakkouzi, Z.S.; Eastman, C.M.: Query formulation for handling negation in information retrieval systems (1990)
0.02
0.024711959 = product of:
0.07413588 = sum of:
0.020155499 = weight(_text_:library in 3531) [ClassicSimilarity], result of:
0.020155499 = score(doc=3531,freq=2.0), product of:
0.08672522 = queryWeight, product of:
2.6293786 = idf(docFreq=8668, maxDocs=44218)
0.03298316 = queryNorm
0.23240642 = fieldWeight in 3531, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
2.6293786 = idf(docFreq=8668, maxDocs=44218)
0.0625 = fieldNorm(doc=3531)
0.018861448 = weight(_text_:of in 3531) [ClassicSimilarity], result of:
0.018861448 = score(doc=3531,freq=14.0), product of:
0.05157766 = queryWeight, product of:
1.5637573 = idf(docFreq=25162, maxDocs=44218)
0.03298316 = queryNorm
0.36569026 = fieldWeight in 3531, product of:
3.7416575 = tf(freq=14.0), with freq of:
14.0 = termFreq=14.0
1.5637573 = idf(docFreq=25162, maxDocs=44218)
0.0625 = fieldNorm(doc=3531)
0.03511893 = product of:
0.07023786 = sum of:
0.07023786 = weight(_text_:problems in 3531) [ClassicSimilarity], result of:
0.07023786 = score(doc=3531,freq=4.0), product of:
0.13613719 = queryWeight, product of:
4.1274753 = idf(docFreq=1937, maxDocs=44218)
0.03298316 = queryNorm
0.5159344 = fieldWeight in 3531, product of:
2.0 = tf(freq=4.0), with freq of:
4.0 = termFreq=4.0
4.1274753 = idf(docFreq=1937, maxDocs=44218)
0.0625 = fieldNorm(doc=3531)
0.5 = coord(1/2)
0.33333334 = coord(3/9)
- Abstract
- Queries containing negation are widely recognised as presenting problems for both users and systems. In information retrieval systems such problems usually manifest themselves in the use of the NOT operator. Describes an algorithm to transform Boolean queries with negated terms into queries without negation; the transformation process is based on the use of a hierarchical thesaurus. Examines a set of user requests submitted to the Thomas Cooper Library at the University of South Carolina to determine the pattern and frequency of use of negation.
- Source
- Journal of the American Society for Information Science. 41(1990) no.3, S.171-182
-
Willett, P.: Best-match text retrieval (1993)
0.02
0.023890018 = product of:
0.071670055 = sum of:
0.025194373 = weight(_text_:library in 7818) [ClassicSimilarity], result of:
0.025194373 = score(doc=7818,freq=2.0), product of:
0.08672522 = queryWeight, product of:
2.6293786 = idf(docFreq=8668, maxDocs=44218)
0.03298316 = queryNorm
0.29050803 = fieldWeight in 7818, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
2.6293786 = idf(docFreq=8668, maxDocs=44218)
0.078125 = fieldNorm(doc=7818)
0.015434646 = weight(_text_:of in 7818) [ClassicSimilarity], result of:
0.015434646 = score(doc=7818,freq=6.0), product of:
0.05157766 = queryWeight, product of:
1.5637573 = idf(docFreq=25162, maxDocs=44218)
0.03298316 = queryNorm
0.2992506 = fieldWeight in 7818, product of:
2.4494898 = tf(freq=6.0), with freq of:
6.0 = termFreq=6.0
1.5637573 = idf(docFreq=25162, maxDocs=44218)
0.078125 = fieldNorm(doc=7818)
0.031041041 = product of:
0.062082082 = sum of:
0.062082082 = weight(_text_:problems in 7818) [ClassicSimilarity], result of:
0.062082082 = score(doc=7818,freq=2.0), product of:
0.13613719 = queryWeight, product of:
4.1274753 = idf(docFreq=1937, maxDocs=44218)
0.03298316 = queryNorm
0.4560259 = fieldWeight in 7818, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
4.1274753 = idf(docFreq=1937, maxDocs=44218)
0.078125 = fieldNorm(doc=7818)
0.5 = coord(1/2)
0.33333334 = coord(3/9)
- Abstract
- Provides an introduction to the computational techniques that underlie best match searching retrieval systems. Discusses: problems of traditional Boolean systems; characteristics of best-match searching; automatic indexing; term conflation; matching of documents and queries (dealing with similarity measures, initial weights, relevance weights, and the matching algorithm); and describes operational best-match systems
- Source
- Library and information briefings. 1993, no.49, S.1-11
-
Robertson, A.M.; Willett, P.: Use of genetic algorithms in information retrieval (1995)
0.02
0.020816881 = product of:
0.06245064 = sum of:
0.020155499 = weight(_text_:library in 2418) [ClassicSimilarity], result of:
0.020155499 = score(doc=2418,freq=2.0), product of:
0.08672522 = queryWeight, product of:
2.6293786 = idf(docFreq=8668, maxDocs=44218)
0.03298316 = queryNorm
0.23240642 = fieldWeight in 2418, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
2.6293786 = idf(docFreq=8668, maxDocs=44218)
0.0625 = fieldNorm(doc=2418)
0.017462308 = weight(_text_:of in 2418) [ClassicSimilarity], result of:
0.017462308 = score(doc=2418,freq=12.0), product of:
0.05157766 = queryWeight, product of:
1.5637573 = idf(docFreq=25162, maxDocs=44218)
0.03298316 = queryNorm
0.33856338 = fieldWeight in 2418, product of:
3.4641016 = tf(freq=12.0), with freq of:
12.0 = termFreq=12.0
1.5637573 = idf(docFreq=25162, maxDocs=44218)
0.0625 = fieldNorm(doc=2418)
0.024832834 = product of:
0.049665667 = sum of:
0.049665667 = weight(_text_:problems in 2418) [ClassicSimilarity], result of:
0.049665667 = score(doc=2418,freq=2.0), product of:
0.13613719 = queryWeight, product of:
4.1274753 = idf(docFreq=1937, maxDocs=44218)
0.03298316 = queryNorm
0.36482072 = fieldWeight in 2418, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
4.1274753 = idf(docFreq=1937, maxDocs=44218)
0.0625 = fieldNorm(doc=2418)
0.5 = coord(1/2)
0.33333334 = coord(3/9)
- Abstract
- Reviews the basic techniques involving genetic algorithms and their application to 2 problems in information retrieval: the generation of equifrequent groups of index terms; and the identification of optimal query and term weights. The algorithm developed for the generation of equifrequent groupings proved to be effective in operation, achieving results comparable with those obtained using a good deterministic algorithm. The algorithm developed for the identification of optimal query and term weighting involves fitness function that is based on full relevance information
- Imprint
- London : British Library Research and Development Department
-
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)
0.02
0.019227866 = product of:
0.057683595 = sum of:
0.015434646 = weight(_text_:of in 6118) [ClassicSimilarity], result of:
0.015434646 = score(doc=6118,freq=24.0), product of:
0.05157766 = queryWeight, product of:
1.5637573 = idf(docFreq=25162, maxDocs=44218)
0.03298316 = queryNorm
0.2992506 = fieldWeight in 6118, product of:
4.8989797 = tf(freq=24.0), with freq of:
24.0 = termFreq=24.0
1.5637573 = idf(docFreq=25162, maxDocs=44218)
0.0390625 = fieldNorm(doc=6118)
0.0155205205 = product of:
0.031041041 = sum of:
0.031041041 = weight(_text_:problems in 6118) [ClassicSimilarity], result of:
0.031041041 = score(doc=6118,freq=2.0), product of:
0.13613719 = queryWeight, product of:
4.1274753 = idf(docFreq=1937, maxDocs=44218)
0.03298316 = queryNorm
0.22801295 = fieldWeight in 6118, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
4.1274753 = idf(docFreq=1937, maxDocs=44218)
0.0390625 = fieldNorm(doc=6118)
0.5 = coord(1/2)
0.026728429 = product of:
0.053456858 = sum of:
0.053456858 = weight(_text_:etc in 6118) [ClassicSimilarity], result of:
0.053456858 = score(doc=6118,freq=2.0), product of:
0.17865302 = queryWeight, product of:
5.4164915 = idf(docFreq=533, maxDocs=44218)
0.03298316 = queryNorm
0.2992217 = fieldWeight in 6118, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
5.4164915 = idf(docFreq=533, maxDocs=44218)
0.0390625 = fieldNorm(doc=6118)
0.5 = coord(1/2)
0.33333334 = coord(3/9)
- 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.
- Source
- Journal of the American Society for Information Science and Technology. 57(2006) no.12, S.1694-1707
-
Lewandowski, D.: How can library materials be ranked in the OPAC? (2009)
0.02
0.018168855 = product of:
0.054506563 = sum of:
0.017815111 = weight(_text_:library in 2810) [ClassicSimilarity], result of:
0.017815111 = score(doc=2810,freq=4.0), product of:
0.08672522 = queryWeight, product of:
2.6293786 = idf(docFreq=8668, maxDocs=44218)
0.03298316 = queryNorm
0.2054202 = fieldWeight in 2810, product of:
2.0 = tf(freq=4.0), with freq of:
4.0 = termFreq=4.0
2.6293786 = idf(docFreq=8668, maxDocs=44218)
0.0390625 = fieldNorm(doc=2810)
0.009963021 = weight(_text_:of in 2810) [ClassicSimilarity], result of:
0.009963021 = score(doc=2810,freq=10.0), product of:
0.05157766 = queryWeight, product of:
1.5637573 = idf(docFreq=25162, maxDocs=44218)
0.03298316 = queryNorm
0.19316542 = fieldWeight in 2810, product of:
3.1622777 = tf(freq=10.0), with freq of:
10.0 = termFreq=10.0
1.5637573 = idf(docFreq=25162, maxDocs=44218)
0.0390625 = fieldNorm(doc=2810)
0.026728429 = product of:
0.053456858 = sum of:
0.053456858 = weight(_text_:etc in 2810) [ClassicSimilarity], result of:
0.053456858 = score(doc=2810,freq=2.0), product of:
0.17865302 = queryWeight, product of:
5.4164915 = idf(docFreq=533, maxDocs=44218)
0.03298316 = queryNorm
0.2992217 = fieldWeight in 2810, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
5.4164915 = idf(docFreq=533, maxDocs=44218)
0.0390625 = fieldNorm(doc=2810)
0.5 = coord(1/2)
0.33333334 = coord(3/9)
- Abstract
- Some Online Public Access Catalogues offer a ranking component. However, ranking there is merely text-based and is doomed to fail due to limited text in bibliographic data. The main assumption for the talk is that we are in a situation where the appropriate ranking factors for OPACs should be defined, while the implementation is no major problem. We must define what we want, and not so much focus on the technical work. Some deep thinking is necessary on the "perfect results set" and how we can achieve it through ranking. The talk presents a set of potential ranking factors and clustering possibilities for further discussion. A look at commercial Web search engines could provide us with ideas how ranking can be improved with additional factors. Search engines are way beyond pure text-based ranking and apply ranking factors in the groups like popularity, freshness, personalisation, etc. The talk describes the main factors used in search engines and how derivatives of these could be used for libraries' purposes. The goal of ranking is to provide the user with the best-suitable results on top of the results list. How can this goal be achieved with the library catalogue and also concerning the library's different collections and databases? The assumption is that ranking of such materials is a complex problem and is yet nowhere near solved. Libraries should focus on ranking to improve user experience.
-
Ravana, S.D.; Rajagopal, P.; Balakrishnan, V.: Ranking retrieval systems using pseudo relevance judgments (2015)
0.02
0.015365845 = product of:
0.046097532 = sum of:
0.014777548 = weight(_text_:of in 2591) [ClassicSimilarity], result of:
0.014777548 = score(doc=2591,freq=22.0), product of:
0.05157766 = queryWeight, product of:
1.5637573 = idf(docFreq=25162, maxDocs=44218)
0.03298316 = queryNorm
0.28651062 = fieldWeight in 2591, product of:
4.690416 = tf(freq=22.0), with freq of:
22.0 = termFreq=22.0
1.5637573 = idf(docFreq=25162, maxDocs=44218)
0.0390625 = fieldNorm(doc=2591)
0.0155205205 = product of:
0.031041041 = sum of:
0.031041041 = weight(_text_:problems in 2591) [ClassicSimilarity], result of:
0.031041041 = score(doc=2591,freq=2.0), product of:
0.13613719 = queryWeight, product of:
4.1274753 = idf(docFreq=1937, maxDocs=44218)
0.03298316 = queryNorm
0.22801295 = fieldWeight in 2591, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
4.1274753 = idf(docFreq=1937, maxDocs=44218)
0.0390625 = fieldNorm(doc=2591)
0.5 = coord(1/2)
0.015799463 = product of:
0.031598926 = sum of:
0.031598926 = weight(_text_:22 in 2591) [ClassicSimilarity], result of:
0.031598926 = score(doc=2591,freq=4.0), product of:
0.11550141 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.03298316 = queryNorm
0.27358043 = fieldWeight in 2591, product of:
2.0 = tf(freq=4.0), with freq of:
4.0 = termFreq=4.0
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.0390625 = fieldNorm(doc=2591)
0.5 = coord(1/2)
0.33333334 = coord(3/9)
- Abstract
- Purpose In a system-based approach, replicating the web would require large test collections, and judging the relevancy of all documents per topic in creating relevance judgment through human assessors is infeasible. Due to the large amount of documents that requires judgment, there are possible errors introduced by human assessors because of disagreements. The paper aims to discuss these issues. Design/methodology/approach This study explores exponential variation and document ranking methods that generate a reliable set of relevance judgments (pseudo relevance judgments) to reduce human efforts. These methods overcome problems with large amounts of documents for judgment while avoiding human disagreement errors during the judgment process. This study utilizes two key factors: number of occurrences of each document per topic from all the system runs; and document rankings to generate the alternate methods. Findings The effectiveness of the proposed method is evaluated using the correlation coefficient of ranked systems using mean average precision scores between the original Text REtrieval Conference (TREC) relevance judgments and pseudo relevance judgments. The results suggest that the proposed document ranking method with a pool depth of 100 could be a reliable alternative to reduce human effort and disagreement errors involved in generating TREC-like relevance judgments. Originality/value Simple methods proposed in this study show improvement in the correlation coefficient in generating alternate relevance judgment without human assessors while contributing to information retrieval evaluation.
- Date
- 20. 1.2015 18:30:22
18. 9.2018 18:22:56
- Source
- Aslib journal of information management. 67(2015) no.6, S.700-714
-
Klas, C.-P.; Fuhr, N.; Schaefer, A.: Evaluating strategic support for information access in the DAFFODIL system (2004)
0.01
0.014681737 = product of:
0.04404521 = sum of:
0.021378135 = weight(_text_:library in 2419) [ClassicSimilarity], result of:
0.021378135 = score(doc=2419,freq=4.0), product of:
0.08672522 = queryWeight, product of:
2.6293786 = idf(docFreq=8668, maxDocs=44218)
0.03298316 = queryNorm
0.24650425 = fieldWeight in 2419, product of:
2.0 = tf(freq=4.0), with freq of:
4.0 = termFreq=4.0
2.6293786 = idf(docFreq=8668, maxDocs=44218)
0.046875 = fieldNorm(doc=2419)
0.009260787 = weight(_text_:of in 2419) [ClassicSimilarity], result of:
0.009260787 = score(doc=2419,freq=6.0), product of:
0.05157766 = queryWeight, product of:
1.5637573 = idf(docFreq=25162, maxDocs=44218)
0.03298316 = queryNorm
0.17955035 = fieldWeight in 2419, product of:
2.4494898 = tf(freq=6.0), with freq of:
6.0 = termFreq=6.0
1.5637573 = idf(docFreq=25162, maxDocs=44218)
0.046875 = fieldNorm(doc=2419)
0.013406289 = product of:
0.026812578 = sum of:
0.026812578 = weight(_text_:22 in 2419) [ClassicSimilarity], result of:
0.026812578 = score(doc=2419,freq=2.0), product of:
0.11550141 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.03298316 = queryNorm
0.23214069 = fieldWeight in 2419, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.046875 = fieldNorm(doc=2419)
0.5 = coord(1/2)
0.33333334 = coord(3/9)
- Abstract
- The digital library system Daffodil is targeted at strategic support of users during the information search process. For searching, exploring and managing digital library objects it provides user-customisable information seeking patterns over a federation of heterogeneous digital libraries. In this paper evaluation results with respect to retrieval effectiveness, efficiency and user satisfaction are presented. The analysis focuses on strategic support for the scientific work-flow. Daffodil supports the whole work-flow, from data source selection over information seeking to the representation, organisation and reuse of information. By embedding high level search functionality into the scientific work-flow, the user experiences better strategic system support due to a more systematic work process. These ideas have been implemented in Daffodil followed by a qualitative evaluation. The evaluation has been conducted with 28 participants, ranging from information seeking novices to experts. The results are promising, as they support the chosen model.
- Date
- 16.11.2008 16:22:48
-
Liu, J.; Liu, C.: Personalization in text information retrieval : a survey (2020)
0.01
0.012990307 = product of:
0.05845638 = sum of:
0.01309673 = weight(_text_:of in 5761) [ClassicSimilarity], result of:
0.01309673 = score(doc=5761,freq=12.0), product of:
0.05157766 = queryWeight, product of:
1.5637573 = idf(docFreq=25162, maxDocs=44218)
0.03298316 = queryNorm
0.25392252 = fieldWeight in 5761, product of:
3.4641016 = tf(freq=12.0), with freq of:
12.0 = termFreq=12.0
1.5637573 = idf(docFreq=25162, maxDocs=44218)
0.046875 = fieldNorm(doc=5761)
0.04535965 = product of:
0.0907193 = sum of:
0.0907193 = weight(_text_:etc in 5761) [ClassicSimilarity], result of:
0.0907193 = score(doc=5761,freq=4.0), product of:
0.17865302 = queryWeight, product of:
5.4164915 = idf(docFreq=533, maxDocs=44218)
0.03298316 = queryNorm
0.50779605 = fieldWeight in 5761, product of:
2.0 = tf(freq=4.0), with freq of:
4.0 = termFreq=4.0
5.4164915 = idf(docFreq=533, maxDocs=44218)
0.046875 = fieldNorm(doc=5761)
0.5 = coord(1/2)
0.22222222 = coord(2/9)
- Abstract
- Personalization of information retrieval (PIR) is aimed at tailoring a search toward individual users and user groups by taking account of additional information about users besides their queries. In the past two decades or so, PIR has received extensive attention in both academia and industry. This article surveys the literature of personalization in text retrieval, following a framework for aspects or factors that can be used for personalization. The framework consists of additional information about users that can be explicitly obtained by asking users for their preferences, or implicitly inferred from users' search behaviors. Users' characteristics and contextual factors such as tasks, time, location, etc., can be helpful for personalization. This article also addresses various issues including when to personalize, the evaluation of PIR, privacy, usability, etc. Based on the extensive review, challenges are discussed and directions for future effort are suggested.
- Source
- Journal of the Association for Information Science and Technology. 71(2020) no.3, S.349-369
-
Langville, A.N.; Meyer, C.D.: Google's PageRank and beyond : the science of search engine rankings (2006)
0.01
0.0109929275 = product of:
0.03297878 = sum of:
0.007558312 = weight(_text_:library in 6) [ClassicSimilarity], result of:
0.007558312 = score(doc=6,freq=2.0), product of:
0.08672522 = queryWeight, product of:
2.6293786 = idf(docFreq=8668, maxDocs=44218)
0.03298316 = queryNorm
0.08715241 = fieldWeight in 6, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
2.6293786 = idf(docFreq=8668, maxDocs=44218)
0.0234375 = fieldNorm(doc=6)
0.01225087 = weight(_text_:of in 6) [ClassicSimilarity], result of:
0.01225087 = score(doc=6,freq=42.0), product of:
0.05157766 = queryWeight, product of:
1.5637573 = idf(docFreq=25162, maxDocs=44218)
0.03298316 = queryNorm
0.23752278 = fieldWeight in 6, product of:
6.4807405 = tf(freq=42.0), with freq of:
42.0 = termFreq=42.0
1.5637573 = idf(docFreq=25162, maxDocs=44218)
0.0234375 = fieldNorm(doc=6)
0.013169598 = product of:
0.026339196 = sum of:
0.026339196 = weight(_text_:problems in 6) [ClassicSimilarity], result of:
0.026339196 = score(doc=6,freq=4.0), product of:
0.13613719 = queryWeight, product of:
4.1274753 = idf(docFreq=1937, maxDocs=44218)
0.03298316 = queryNorm
0.1934754 = fieldWeight in 6, product of:
2.0 = tf(freq=4.0), with freq of:
4.0 = termFreq=4.0
4.1274753 = idf(docFreq=1937, maxDocs=44218)
0.0234375 = fieldNorm(doc=6)
0.5 = coord(1/2)
0.33333334 = coord(3/9)
- Abstract
- Why doesn't your home page appear on the first page of search results, even when you query your own name? How do other Web pages always appear at the top? What creates these powerful rankings? And how? The first book ever about the science of Web page rankings, "Google's PageRank and Beyond" supplies the answers to these and other questions and more. The book serves two very different audiences: the curious science reader and the technical computational reader. The chapters build in mathematical sophistication, so that the first five are accessible to the general academic reader. While other chapters are much more mathematical in nature, each one contains something for both audiences. For example, the authors include entertaining asides such as how search engines make money and how the Great Firewall of China influences research. The book includes an extensive background chapter designed to help readers learn more about the mathematics of search engines, and it contains several MATLAB codes and links to sample Web data sets. The philosophy throughout is to encourage readers to experiment with the ideas and algorithms in the text. Any business seriously interested in improving its rankings in the major search engines can benefit from the clear examples, sample code, and list of resources provided. It includes: many illustrative examples and entertaining asides; MATLAB code; accessible and informal style; and complete and self-contained section for mathematics review.
- Content
- Inhalt: Chapter 1. Introduction to Web Search Engines: 1.1 A Short History of Information Retrieval - 1.2 An Overview of Traditional Information Retrieval - 1.3 Web Information Retrieval Chapter 2. Crawling, Indexing, and Query Processing: 2.1 Crawling - 2.2 The Content Index - 2.3 Query Processing Chapter 3. Ranking Webpages by Popularity: 3.1 The Scene in 1998 - 3.2 Two Theses - 3.3 Query-Independence Chapter 4. The Mathematics of Google's PageRank: 4.1 The Original Summation Formula for PageRank - 4.2 Matrix Representation of the Summation Equations - 4.3 Problems with the Iterative Process - 4.4 A Little Markov Chain Theory - 4.5 Early Adjustments to the Basic Model - 4.6 Computation of the PageRank Vector - 4.7 Theorem and Proof for Spectrum of the Google Matrix Chapter 5. Parameters in the PageRank Model: 5.1 The a Factor - 5.2 The Hyperlink Matrix H - 5.3 The Teleportation Matrix E Chapter 6. The Sensitivity of PageRank; 6.1 Sensitivity with respect to alpha - 6.2 Sensitivity with respect to H - 6.3 Sensitivity with respect to vT - 6.4 Other Analyses of Sensitivity - 6.5 Sensitivity Theorems and Proofs Chapter 7. The PageRank Problem as a Linear System: 7.1 Properties of (I - alphaS) - 7.2 Properties of (I - alphaH) - 7.3 Proof of the PageRank Sparse Linear System Chapter 8. Issues in Large-Scale Implementation of PageRank: 8.1 Storage Issues - 8.2 Convergence Criterion - 8.3 Accuracy - 8.4 Dangling Nodes - 8.5 Back Button Modeling
Chapter 9. Accelerating the Computation of PageRank: 9.1 An Adaptive Power Method - 9.2 Extrapolation - 9.3 Aggregation - 9.4 Other Numerical Methods Chapter 10. Updating the PageRank Vector: 10.1 The Two Updating Problems and their History - 10.2 Restarting the Power Method - 10.3 Approximate Updating Using Approximate Aggregation - 10.4 Exact Aggregation - 10.5 Exact vs. Approximate Aggregation - 10.6 Updating with Iterative Aggregation - 10.7 Determining the Partition - 10.8 Conclusions Chapter 11. The HITS Method for Ranking Webpages: 11.1 The HITS Algorithm - 11.2 HITS Implementation - 11.3 HITS Convergence - 11.4 HITS Example - 11.5 Strengths and Weaknesses of HITS - 11.6 HITS's Relationship to Bibliometrics - 11.7 Query-Independent HITS - 11.8 Accelerating HITS - 11.9 HITS Sensitivity Chapter 12. Other Link Methods for Ranking Webpages: 12.1 SALSA - 12.2 Hybrid Ranking Methods - 12.3 Rankings based on Traffic Flow Chapter 13. The Future of Web Information Retrieval: 13.1 Spam - 13.2 Personalization - 13.3 Clustering - 13.4 Intelligent Agents - 13.5 Trends and Time-Sensitive Search - 13.6 Privacy and Censorship - 13.7 Library Classification Schemes - 13.8 Data Fusion Chapter 14. Resources for Web Information Retrieval: 14.1 Resources for Getting Started - 14.2 Resources for Serious Study Chapter 15. The Mathematics Guide: 15.1 Linear Algebra - 15.2 Perron-Frobenius Theory - 15.3 Markov Chains - 15.4 Perron Complementation - 15.5 Stochastic Complementation - 15.6 Censoring - 15.7 Aggregation - 15.8 Disaggregation
-
Tseng, Y.H.; Lin, Y.I.: Evaluation of fuzzy search, term suggestion, and term relevance feedback in an OPAC system (1998)
0.01
0.010834404 = product of:
0.04875482 = sum of:
0.030233247 = weight(_text_:library in 6430) [ClassicSimilarity], result of:
0.030233247 = score(doc=6430,freq=2.0), product of:
0.08672522 = queryWeight, product of:
2.6293786 = idf(docFreq=8668, maxDocs=44218)
0.03298316 = queryNorm
0.34860963 = fieldWeight in 6430, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
2.6293786 = idf(docFreq=8668, maxDocs=44218)
0.09375 = fieldNorm(doc=6430)
0.018521573 = weight(_text_:of in 6430) [ClassicSimilarity], result of:
0.018521573 = score(doc=6430,freq=6.0), product of:
0.05157766 = queryWeight, product of:
1.5637573 = idf(docFreq=25162, maxDocs=44218)
0.03298316 = queryNorm
0.3591007 = fieldWeight in 6430, product of:
2.4494898 = tf(freq=6.0), with freq of:
6.0 = termFreq=6.0
1.5637573 = idf(docFreq=25162, maxDocs=44218)
0.09375 = fieldNorm(doc=6430)
0.22222222 = coord(2/9)
- Source
- Bulletin of the Library Association of China. 1998, no.61, S.103-125
-
Jones, G.; Robertson, A.M.; Willett, P.: ¬An introduction to genetic algorithms and to their use in information retrieval (1994)
0.01
0.010044622 = product of:
0.0452008 = sum of:
0.010081868 = weight(_text_:of in 7415) [ClassicSimilarity], result of:
0.010081868 = score(doc=7415,freq=4.0), product of:
0.05157766 = queryWeight, product of:
1.5637573 = idf(docFreq=25162, maxDocs=44218)
0.03298316 = queryNorm
0.19546966 = fieldWeight in 7415, product of:
2.0 = tf(freq=4.0), with freq of:
4.0 = termFreq=4.0
1.5637573 = idf(docFreq=25162, maxDocs=44218)
0.0625 = fieldNorm(doc=7415)
0.03511893 = product of:
0.07023786 = sum of:
0.07023786 = weight(_text_:problems in 7415) [ClassicSimilarity], result of:
0.07023786 = score(doc=7415,freq=4.0), product of:
0.13613719 = queryWeight, product of:
4.1274753 = idf(docFreq=1937, maxDocs=44218)
0.03298316 = queryNorm
0.5159344 = fieldWeight in 7415, product of:
2.0 = tf(freq=4.0), with freq of:
4.0 = termFreq=4.0
4.1274753 = idf(docFreq=1937, maxDocs=44218)
0.0625 = fieldNorm(doc=7415)
0.5 = coord(1/2)
0.22222222 = coord(2/9)
- Abstract
- This paper provides an introduction to genetic algorithms, a new approach to the investigation of computationally-intensive problems that may be insoluble using conventional, deterministic approaches. A genetic algorithm takes an initial set of possible starting solutions and then iteratively improves theses solutions using operators that are analogous to those involved in Darwinian evolution. The approach is illusrated by reference to several problems in information retrieval
-
Smeaton, A.F.; Rijsbergen, C.J. van: ¬The retrieval effects of query expansion on a feedback document retrieval system (1983)
0.01
0.009723781 = product of:
0.043757014 = sum of:
0.012475675 = weight(_text_:of in 2134) [ClassicSimilarity], result of:
0.012475675 = score(doc=2134,freq=2.0), product of:
0.05157766 = queryWeight, product of:
1.5637573 = idf(docFreq=25162, maxDocs=44218)
0.03298316 = queryNorm
0.24188137 = fieldWeight in 2134, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
1.5637573 = idf(docFreq=25162, maxDocs=44218)
0.109375 = fieldNorm(doc=2134)
0.03128134 = product of:
0.06256268 = sum of:
0.06256268 = weight(_text_:22 in 2134) [ClassicSimilarity], result of:
0.06256268 = score(doc=2134,freq=2.0), product of:
0.11550141 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.03298316 = queryNorm
0.5416616 = fieldWeight in 2134, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.109375 = fieldNorm(doc=2134)
0.5 = coord(1/2)
0.22222222 = coord(2/9)
- Date
- 30. 3.2001 13:32:22
-
Zhang, W.; Korf, R.E.: Performance of linear-space search algorithms (1995)
0.01
0.009698529 = product of:
0.043643378 = sum of:
0.012602335 = weight(_text_:of in 4744) [ClassicSimilarity], result of:
0.012602335 = score(doc=4744,freq=4.0), product of:
0.05157766 = queryWeight, product of:
1.5637573 = idf(docFreq=25162, maxDocs=44218)
0.03298316 = queryNorm
0.24433708 = fieldWeight in 4744, product of:
2.0 = tf(freq=4.0), with freq of:
4.0 = termFreq=4.0
1.5637573 = idf(docFreq=25162, maxDocs=44218)
0.078125 = fieldNorm(doc=4744)
0.031041041 = product of:
0.062082082 = sum of:
0.062082082 = weight(_text_:problems in 4744) [ClassicSimilarity], result of:
0.062082082 = score(doc=4744,freq=2.0), product of:
0.13613719 = queryWeight, product of:
4.1274753 = idf(docFreq=1937, maxDocs=44218)
0.03298316 = queryNorm
0.4560259 = fieldWeight in 4744, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
4.1274753 = idf(docFreq=1937, maxDocs=44218)
0.078125 = fieldNorm(doc=4744)
0.5 = coord(1/2)
0.22222222 = coord(2/9)
- Abstract
- Search algorithms in artificial intelligence systems that use space linear in the search depth are employed in practice to solve difficult problems optimally, such as planning and scheduling. Studies the average-case performance of linear-space search algorithms, including depth-first branch-and-bound, iterative-deepening, and recursive best-first search
-
Green, R.: Topical relevance relationships : 2: an exploratory study and preliminary typology (1995)
0.01
0.009655196 = product of:
0.04344838 = sum of:
0.025715325 = product of:
0.05143065 = sum of:
0.05143065 = weight(_text_:headings in 3724) [ClassicSimilarity], result of:
0.05143065 = score(doc=3724,freq=2.0), product of:
0.15996648 = queryWeight, product of:
4.849944 = idf(docFreq=940, maxDocs=44218)
0.03298316 = queryNorm
0.3215089 = fieldWeight in 3724, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
4.849944 = idf(docFreq=940, maxDocs=44218)
0.046875 = fieldNorm(doc=3724)
0.5 = coord(1/2)
0.017733058 = weight(_text_:of in 3724) [ClassicSimilarity], result of:
0.017733058 = score(doc=3724,freq=22.0), product of:
0.05157766 = queryWeight, product of:
1.5637573 = idf(docFreq=25162, maxDocs=44218)
0.03298316 = queryNorm
0.34381276 = fieldWeight in 3724, product of:
4.690416 = tf(freq=22.0), with freq of:
22.0 = termFreq=22.0
1.5637573 = idf(docFreq=25162, maxDocs=44218)
0.046875 = fieldNorm(doc=3724)
0.22222222 = coord(2/9)
- Abstract
- The assumption of topic matching between user needs and texts topically relevant to those needs is often erroneous. Reports an emprical investigantion of the question 'what relationship types actually account for topical relevance'? In order to avoid the bias to topic matching search strategies, user needs are back generated from a randomly selected subset of the subject headings employed in a user oriented topical concordance. The corresponding relevant texts are those indicated in the concordance under the subject heading. Compares the topics of the user needs with the topics of the relevant texts to determine the relationships between them. Topical relevance relationships include a large variety of relationships, only some of which are matching relationships. Others are examples of paradigmatic or syntagmatic relationships. There appear to be no constraints on the kinds of relationships that can function as topical relevance relationships. They are distinguishable from other types of relationships only on functional grounds
- Source
- Journal of the American Society for Information Science. 46(1995) no.9, S.654-662
-
Maron, M.E.; Kuhns, I.L.: On relevance, probabilistic indexing and information retrieval (1960)
0.01
0.009559282 = product of:
0.04301677 = sum of:
0.025194373 = weight(_text_:library in 1928) [ClassicSimilarity], result of:
0.025194373 = score(doc=1928,freq=8.0), product of:
0.08672522 = queryWeight, product of:
2.6293786 = idf(docFreq=8668, maxDocs=44218)
0.03298316 = queryNorm
0.29050803 = fieldWeight in 1928, product of:
2.828427 = tf(freq=8.0), with freq of:
8.0 = termFreq=8.0
2.6293786 = idf(docFreq=8668, maxDocs=44218)
0.0390625 = fieldNorm(doc=1928)
0.017822394 = weight(_text_:of in 1928) [ClassicSimilarity], result of:
0.017822394 = score(doc=1928,freq=32.0), product of:
0.05157766 = queryWeight, product of:
1.5637573 = idf(docFreq=25162, maxDocs=44218)
0.03298316 = queryNorm
0.34554482 = fieldWeight in 1928, product of:
5.656854 = tf(freq=32.0), with freq of:
32.0 = termFreq=32.0
1.5637573 = idf(docFreq=25162, maxDocs=44218)
0.0390625 = fieldNorm(doc=1928)
0.22222222 = coord(2/9)
- Abstract
- Reports on a novel technique for literature indexing and searching in a mechanized library system. The notion of relevance is taken as the key concept in the theory of information retrieval and a comparative concept of relevance is explicated in terms of the theory of probability. The resulting technique called 'Probabilistic indexing' allows a computing machine, given a request for information, to make a statistical inference and derive a number (called the 'relevance number') for each document, which is a measure of the probability that the document will satisfy the given request. The result of a search is an ordered list of those documents which satisfy the request ranked according to their probable relevance. The paper goes on to show that whereas in a conventional library system the cross-referencing ('see' and 'see also') is based soley on the 'semantic closeness' between index terms, statistical measures of closeness between index terms can be defined and computed. Thus, given an arbitrary request consisting of one (or many) index term(s), a machine can eleborate on it to increase the probability of selecting relevant documents that would not otherwise have been selected. Finally, the paper suggest an interpretation of the whole library problem as one where the request is considered as a clue on the basis of which the library system makes a concatenated statistical inference in order to provide as an output an ordered list of those documents which most probably satisfy the information needs of the user
- Source
- Journal of the Association for Computing Machinery. 7(1960) no.3, S.216-244
-
Keen, E.M.: Interactive ranked retrieval (1995)
0.01
0.009209983 = product of:
0.041444924 = sum of:
0.024941156 = weight(_text_:library in 2419) [ClassicSimilarity], result of:
0.024941156 = score(doc=2419,freq=4.0), product of:
0.08672522 = queryWeight, product of:
2.6293786 = idf(docFreq=8668, maxDocs=44218)
0.03298316 = queryNorm
0.28758827 = fieldWeight in 2419, product of:
2.0 = tf(freq=4.0), with freq of:
4.0 = termFreq=4.0
2.6293786 = idf(docFreq=8668, maxDocs=44218)
0.0546875 = fieldNorm(doc=2419)
0.016503768 = weight(_text_:of in 2419) [ClassicSimilarity], result of:
0.016503768 = score(doc=2419,freq=14.0), product of:
0.05157766 = queryWeight, product of:
1.5637573 = idf(docFreq=25162, maxDocs=44218)
0.03298316 = queryNorm
0.31997898 = fieldWeight in 2419, product of:
3.7416575 = tf(freq=14.0), with freq of:
14.0 = termFreq=14.0
1.5637573 = idf(docFreq=25162, maxDocs=44218)
0.0546875 = fieldNorm(doc=2419)
0.22222222 = coord(2/9)
- Abstract
- Reports the design, building and testing of the Interactive Ranked Output Search Engine (IROSE), which includes as the main features: query reformulation, ranked output match options, field bias options, marking of must, minus, and truncated suppressed terms. Both DOS and Windows versions of IROSE were constructed and laboratory search tests were performed using 3 test collections of records with queries and relevance jedgements in the subject area of cystic fibrosis, library and information and current affairs. Concludes that there is substantial evidence of the quality of this approach to information retrieval and future tests are needed to redefine and improve the optionality and move to semi operational testing
- Imprint
- London : British Library Research and Development Department
-
Robertson, S.E.: OKAPI at TREC-1 (1994)
0.01
0.00902867 = product of:
0.040629018 = sum of:
0.025194373 = weight(_text_:library in 7953) [ClassicSimilarity], result of:
0.025194373 = score(doc=7953,freq=2.0), product of:
0.08672522 = queryWeight, product of:
2.6293786 = idf(docFreq=8668, maxDocs=44218)
0.03298316 = queryNorm
0.29050803 = fieldWeight in 7953, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
2.6293786 = idf(docFreq=8668, maxDocs=44218)
0.078125 = fieldNorm(doc=7953)
0.015434646 = weight(_text_:of in 7953) [ClassicSimilarity], result of:
0.015434646 = score(doc=7953,freq=6.0), product of:
0.05157766 = queryWeight, product of:
1.5637573 = idf(docFreq=25162, maxDocs=44218)
0.03298316 = queryNorm
0.2992506 = fieldWeight in 7953, product of:
2.4494898 = tf(freq=6.0), with freq of:
6.0 = termFreq=6.0
1.5637573 = idf(docFreq=25162, maxDocs=44218)
0.078125 = fieldNorm(doc=7953)
0.22222222 = coord(2/9)
- Abstract
- Describes the work carried out on the TREC-2 project following the results of the TREC-1 project. Experiments were conducted on the OKAPI experimental text information retrieval system which investigated a number of alternative probabilistic term weighting functions in place of the 'standard' Robertson Sparck Jones weighting functions used in TREC-1
- Imprint
- London : British Library
-
Hancock-Beaulieu, M.; Walker, S.: ¬An evaluation of automatic query expansion in an online library catalogue (1992)
0.01
0.008937928 = product of:
0.040220674 = sum of:
0.024941156 = weight(_text_:library in 2731) [ClassicSimilarity], result of:
0.024941156 = score(doc=2731,freq=4.0), product of:
0.08672522 = queryWeight, product of:
2.6293786 = idf(docFreq=8668, maxDocs=44218)
0.03298316 = queryNorm
0.28758827 = fieldWeight in 2731, product of:
2.0 = tf(freq=4.0), with freq of:
4.0 = termFreq=4.0
2.6293786 = idf(docFreq=8668, maxDocs=44218)
0.0546875 = fieldNorm(doc=2731)
0.015279518 = weight(_text_:of in 2731) [ClassicSimilarity], result of:
0.015279518 = score(doc=2731,freq=12.0), product of:
0.05157766 = queryWeight, product of:
1.5637573 = idf(docFreq=25162, maxDocs=44218)
0.03298316 = queryNorm
0.29624295 = fieldWeight in 2731, product of:
3.4641016 = tf(freq=12.0), with freq of:
12.0 = termFreq=12.0
1.5637573 = idf(docFreq=25162, maxDocs=44218)
0.0546875 = fieldNorm(doc=2731)
0.22222222 = coord(2/9)
- Abstract
- An automatic query expansion (AQE) facility in anonline catalogue was evaluated in an operational library setting. The OKAPI experimental system had other features including: ranked output 'best match' keyword searching, automatic stemming, spelling normalisation and cross referencing as well as relevance feedback. A combination of transaction log analysis, search replays, questionnaires and interviews was used for data collection. Findings show that contrary to previous results, AQE was beneficial in a substantial number of searches. Use intentions, the effectiveness of the 'best match' search and user interaction were identified as the main factors affecting the take-up of the query expansion facility
- Source
- Journal of documentation. 48(1992) no.4, S.406-421
