-
Soulier, L.; Jabeur, L.B.; Tamine, L.; Bahsoun, W.: On ranking relevant entities in heterogeneous networks using a language-based model (2013)
0.12
0.12050651 = sum of:
0.021106426 = product of:
0.0844257 = sum of:
0.0844257 = weight(_text_:authors in 664) [ClassicSimilarity], result of:
0.0844257 = score(doc=664,freq=4.0), product of:
0.23704608 = queryWeight, product of:
4.558814 = idf(docFreq=1258, maxDocs=44218)
0.05199731 = queryNorm
0.35615736 = fieldWeight in 664, product of:
2.0 = tf(freq=4.0), with freq of:
4.0 = termFreq=4.0
4.558814 = idf(docFreq=1258, maxDocs=44218)
0.0390625 = fieldNorm(doc=664)
0.25 = coord(1/4)
0.09940009 = sum of:
0.064175494 = weight(_text_:l in 664) [ClassicSimilarity], result of:
0.064175494 = score(doc=664,freq=4.0), product of:
0.20667124 = queryWeight, product of:
3.9746525 = idf(docFreq=2257, maxDocs=44218)
0.05199731 = queryNorm
0.31051973 = fieldWeight in 664, product of:
2.0 = tf(freq=4.0), with freq of:
4.0 = termFreq=4.0
3.9746525 = idf(docFreq=2257, maxDocs=44218)
0.0390625 = fieldNorm(doc=664)
0.03522459 = weight(_text_:22 in 664) [ClassicSimilarity], result of:
0.03522459 = score(doc=664,freq=2.0), product of:
0.18208572 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.05199731 = queryNorm
0.19345059 = fieldWeight in 664, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.0390625 = fieldNorm(doc=664)
- Abstract
- A new challenge, accessing multiple relevant entities, arises from the availability of linked heterogeneous data. In this article, we address more specifically the problem of accessing relevant entities, such as publications and authors within a bibliographic network, given an information need. We propose a novel algorithm, called BibRank, that estimates a joint relevance of documents and authors within a bibliographic network. This model ranks each type of entity using a score propagation algorithm with respect to the query topic and the structure of the underlying bi-type information entity network. Evidence sources, namely content-based and network-based scores, are both used to estimate the topical similarity between connected entities. For this purpose, authorship relationships are analyzed through a language model-based score on the one hand and on the other hand, non topically related entities of the same type are detected through marginal citations. The article reports the results of experiments using the Bibrank algorithm for an information retrieval task. The CiteSeerX bibliographic data set forms the basis for the topical query automatic generation and evaluation. We show that a statistically significant improvement over closely related ranking models is achieved.
- Date
- 22. 3.2013 19:34:49
-
Bornmann, L.; Mutz, R.: From P100 to P100' : a new citation-rank approach (2014)
0.06
0.064482816 = product of:
0.12896563 = sum of:
0.12896563 = sum of:
0.07260629 = weight(_text_:l in 1431) [ClassicSimilarity], result of:
0.07260629 = score(doc=1431,freq=2.0), product of:
0.20667124 = queryWeight, product of:
3.9746525 = idf(docFreq=2257, maxDocs=44218)
0.05199731 = queryNorm
0.35131297 = fieldWeight in 1431, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.9746525 = idf(docFreq=2257, maxDocs=44218)
0.0625 = fieldNorm(doc=1431)
0.056359343 = weight(_text_:22 in 1431) [ClassicSimilarity], result of:
0.056359343 = score(doc=1431,freq=2.0), product of:
0.18208572 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.05199731 = queryNorm
0.30952093 = fieldWeight in 1431, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.0625 = fieldNorm(doc=1431)
0.5 = coord(1/2)
- Date
- 22. 8.2014 17:05:18
-
Tober, M.; Hennig, L.; Furch, D.: SEO Ranking-Faktoren und Rang-Korrelationen 2014 : Google Deutschland (2014)
0.06
0.064482816 = product of:
0.12896563 = sum of:
0.12896563 = sum of:
0.07260629 = weight(_text_:l in 1484) [ClassicSimilarity], result of:
0.07260629 = score(doc=1484,freq=2.0), product of:
0.20667124 = queryWeight, product of:
3.9746525 = idf(docFreq=2257, maxDocs=44218)
0.05199731 = queryNorm
0.35131297 = fieldWeight in 1484, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.9746525 = idf(docFreq=2257, maxDocs=44218)
0.0625 = fieldNorm(doc=1484)
0.056359343 = weight(_text_:22 in 1484) [ClassicSimilarity], result of:
0.056359343 = score(doc=1484,freq=2.0), product of:
0.18208572 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.05199731 = queryNorm
0.30952093 = fieldWeight in 1484, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.0625 = fieldNorm(doc=1484)
0.5 = coord(1/2)
- Date
- 13. 9.2014 14:45:22
-
Biskri, I.; Rompré, L.: Using association rules for query reformulation (2012)
0.05
0.05255507 = sum of:
0.02532771 = product of:
0.10131084 = sum of:
0.10131084 = weight(_text_:authors in 92) [ClassicSimilarity], result of:
0.10131084 = score(doc=92,freq=4.0), product of:
0.23704608 = queryWeight, product of:
4.558814 = idf(docFreq=1258, maxDocs=44218)
0.05199731 = queryNorm
0.42738882 = fieldWeight in 92, product of:
2.0 = tf(freq=4.0), with freq of:
4.0 = termFreq=4.0
4.558814 = idf(docFreq=1258, maxDocs=44218)
0.046875 = fieldNorm(doc=92)
0.25 = coord(1/4)
0.027227357 = product of:
0.054454714 = sum of:
0.054454714 = weight(_text_:l in 92) [ClassicSimilarity], result of:
0.054454714 = score(doc=92,freq=2.0), product of:
0.20667124 = queryWeight, product of:
3.9746525 = idf(docFreq=2257, maxDocs=44218)
0.05199731 = queryNorm
0.26348472 = fieldWeight in 92, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.9746525 = idf(docFreq=2257, maxDocs=44218)
0.046875 = fieldNorm(doc=92)
0.5 = coord(1/2)
- Abstract
- In this paper the authors will present research on the combination of two methods of data mining: text classification and maximal association rules. Text classification has been the focus of interest of many researchers for a long time. However, the results take the form of lists of words (classes) that people often do not know what to do with. The use of maximal association rules induced a number of advantages: (1) the detection of dependencies and correlations between the relevant units of information (words) of different classes, (2) the extraction of hidden knowledge, often relevant, from a large volume of data. The authors will show how this combination can improve the process of information retrieval.
-
Information retrieval : data structures and algorithms (1992)
0.04
0.03761396 = sum of:
0.014924496 = product of:
0.059697986 = sum of:
0.059697986 = weight(_text_:authors in 3495) [ClassicSimilarity], result of:
0.059697986 = score(doc=3495,freq=2.0), product of:
0.23704608 = queryWeight, product of:
4.558814 = idf(docFreq=1258, maxDocs=44218)
0.05199731 = queryNorm
0.25184128 = fieldWeight in 3495, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
4.558814 = idf(docFreq=1258, maxDocs=44218)
0.0390625 = fieldNorm(doc=3495)
0.25 = coord(1/4)
0.022689465 = product of:
0.04537893 = sum of:
0.04537893 = weight(_text_:l in 3495) [ClassicSimilarity], result of:
0.04537893 = score(doc=3495,freq=2.0), product of:
0.20667124 = queryWeight, product of:
3.9746525 = idf(docFreq=2257, maxDocs=44218)
0.05199731 = queryNorm
0.2195706 = fieldWeight in 3495, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.9746525 = idf(docFreq=2257, maxDocs=44218)
0.0390625 = fieldNorm(doc=3495)
0.5 = coord(1/2)
- Abstract
- The book consists of separate chapters by some 20 different authors. It covers many of the information retrieval algorithms, including methods of file organization, file search and access, and query processing
- Content
- An edited volume containing data structures and algorithms for information retrieval including a disk with examples written in C. for prgrammers and students interested in parsing text, automated indexing, its the first collection in book form of the basic data structures and algorithms that are critical to the storage and retrieval of documents. ------------------Enthält die Kapitel: FRAKES, W.B.: Introduction to information storage and retrieval systems; BAEZA-YATES, R.S.: Introduction to data structures and algorithms related to information retrieval; HARMAN, D. u.a.: Inverted files; FALOUTSOS, C.: Signature files; GONNET, G.H. u.a.: New indices for text: PAT trees and PAT arrays; FORD, D.A. u. S. CHRISTODOULAKIS: File organizations for optical disks; FOX, C.: Lexical analysis and stoplists; FRAKES, W.B.: Stemming algorithms; SRINIVASAN, P.: Thesaurus construction; BAEZA-YATES, R.A.: String searching algorithms; HARMAN, D.: Relevance feedback and other query modification techniques; WARTIK, S.: Boolean operators; WARTIK, S. u.a.: Hashing algorithms; HARMAN, D.: Ranking algorithms; FOX, E.: u.a.: Extended Boolean models; RASMUSSEN, E.: Clustering algorithms; HOLLAAR, L.: Special-purpose hardware for information retrieval; STANFILL, C.: Parallel information retrieval algorithms
-
Voorhees, E.M.: Implementing agglomerative hierarchic clustering algorithms for use in document retrieval (1986)
0.03
0.028179672 = product of:
0.056359343 = sum of:
0.056359343 = product of:
0.11271869 = sum of:
0.11271869 = weight(_text_:22 in 402) [ClassicSimilarity], result of:
0.11271869 = score(doc=402,freq=2.0), product of:
0.18208572 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.05199731 = queryNorm
0.61904186 = fieldWeight in 402, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.125 = fieldNorm(doc=402)
0.5 = coord(1/2)
0.5 = coord(1/2)
- Source
- Information processing and management. 22(1986) no.6, S.465-476
-
Smeaton, A.F.; Rijsbergen, C.J. van: ¬The retrieval effects of query expansion on a feedback document retrieval system (1983)
0.02
0.024657212 = product of:
0.049314424 = sum of:
0.049314424 = product of:
0.09862885 = sum of:
0.09862885 = weight(_text_:22 in 2134) [ClassicSimilarity], result of:
0.09862885 = score(doc=2134,freq=2.0), product of:
0.18208572 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.05199731 = 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)
0.5 = coord(1/2)
- Date
- 30. 3.2001 13:32:22
-
Back, J.: ¬An evaluation of relevancy ranking techniques used by Internet search engines (2000)
0.02
0.024657212 = product of:
0.049314424 = sum of:
0.049314424 = product of:
0.09862885 = sum of:
0.09862885 = weight(_text_:22 in 3445) [ClassicSimilarity], result of:
0.09862885 = score(doc=3445,freq=2.0), product of:
0.18208572 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.05199731 = 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)
0.5 = coord(1/2)
- Date
- 25. 8.2005 17:42:22
-
Khoo, C.S.G.; Wan, K.-W.: ¬A simple relevancy-ranking strategy for an interface to Boolean OPACs (2004)
0.02
0.022775754 = sum of:
0.010447147 = product of:
0.04178859 = sum of:
0.04178859 = weight(_text_:authors in 2509) [ClassicSimilarity], result of:
0.04178859 = score(doc=2509,freq=2.0), product of:
0.23704608 = queryWeight, product of:
4.558814 = idf(docFreq=1258, maxDocs=44218)
0.05199731 = queryNorm
0.17628889 = fieldWeight in 2509, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
4.558814 = idf(docFreq=1258, maxDocs=44218)
0.02734375 = fieldNorm(doc=2509)
0.25 = coord(1/4)
0.012328606 = product of:
0.024657212 = sum of:
0.024657212 = weight(_text_:22 in 2509) [ClassicSimilarity], result of:
0.024657212 = score(doc=2509,freq=2.0), product of:
0.18208572 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.05199731 = 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)
- 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.
- Source
- Electronic library. 22(2004) no.2, S.112-120
-
Ding, Y.; Yan, E.; Frazho, A.; Caverlee, J.: PageRank for ranking authors in co-citation networks (2009)
0.02
0.02193444 = product of:
0.04386888 = sum of:
0.04386888 = product of:
0.17547552 = sum of:
0.17547552 = weight(_text_:authors in 3161) [ClassicSimilarity], result of:
0.17547552 = score(doc=3161,freq=12.0), product of:
0.23704608 = queryWeight, product of:
4.558814 = idf(docFreq=1258, maxDocs=44218)
0.05199731 = queryNorm
0.7402591 = fieldWeight in 3161, product of:
3.4641016 = tf(freq=12.0), with freq of:
12.0 = termFreq=12.0
4.558814 = idf(docFreq=1258, maxDocs=44218)
0.046875 = fieldNorm(doc=3161)
0.25 = coord(1/4)
0.5 = coord(1/2)
- Abstract
- This paper studies how varied damping factors in the PageRank algorithm influence the ranking of authors and proposes weighted PageRank algorithms. We selected the 108 most highly cited authors in the information retrieval (IR) area from the 1970s to 2008 to form the author co-citation network. We calculated the ranks of these 108 authors based on PageRank with the damping factor ranging from 0.05 to 0.95. In order to test the relationship between different measures, we compared PageRank and weighted PageRank results with the citation ranking, h-index, and centrality measures. We found that in our author co-citation network, citation rank is highly correlated with PageRank with different damping factors and also with different weighted PageRank algorithms; citation rank and PageRank are not significantly correlated with centrality measures; and h-index rank does not significantly correlate with centrality measures but does significantly correlate with other measures. The key factors that have impact on the PageRank of authors in the author co-citation network are being co-cited with important authors.
-
Fuhr, N.: Ranking-Experimente mit gewichteter Indexierung (1986)
0.02
0.021134753 = product of:
0.042269506 = sum of:
0.042269506 = product of:
0.08453901 = sum of:
0.08453901 = weight(_text_:22 in 58) [ClassicSimilarity], result of:
0.08453901 = score(doc=58,freq=2.0), product of:
0.18208572 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.05199731 = queryNorm
0.46428138 = fieldWeight in 58, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.09375 = fieldNorm(doc=58)
0.5 = coord(1/2)
0.5 = coord(1/2)
- Date
- 14. 6.2015 22:12:44
-
Fuhr, N.: Rankingexperimente mit gewichteter Indexierung (1986)
0.02
0.021134753 = product of:
0.042269506 = sum of:
0.042269506 = product of:
0.08453901 = sum of:
0.08453901 = weight(_text_:22 in 2051) [ClassicSimilarity], result of:
0.08453901 = score(doc=2051,freq=2.0), product of:
0.18208572 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.05199731 = queryNorm
0.46428138 = fieldWeight in 2051, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.09375 = fieldNorm(doc=2051)
0.5 = coord(1/2)
0.5 = coord(1/2)
- Date
- 14. 6.2015 22:12:56
-
Schamber, L.; Bateman, J.: Relevance criteria uses and importance : progress in development of a measurement scale (1999)
0.02
0.019252649 = product of:
0.038505297 = sum of:
0.038505297 = product of:
0.077010594 = sum of:
0.077010594 = weight(_text_:l in 6691) [ClassicSimilarity], result of:
0.077010594 = score(doc=6691,freq=4.0), product of:
0.20667124 = queryWeight, product of:
3.9746525 = idf(docFreq=2257, maxDocs=44218)
0.05199731 = queryNorm
0.37262368 = fieldWeight in 6691, product of:
2.0 = tf(freq=4.0), with freq of:
4.0 = termFreq=4.0
3.9746525 = idf(docFreq=2257, maxDocs=44218)
0.046875 = fieldNorm(doc=6691)
0.5 = coord(1/2)
0.5 = coord(1/2)
- Source
- Knowledge: creation, organization and use. Proceedings of the 62nd Annual Meeting of the American Society for Information Science, 31.10.-4.11.1999. Ed.: L. Woods
-
Sakai, T.: On the reliability of information retrieval metrics based on graded relevance (2007)
0.02
0.019252649 = product of:
0.038505297 = sum of:
0.038505297 = product of:
0.077010594 = sum of:
0.077010594 = weight(_text_:l in 910) [ClassicSimilarity], result of:
0.077010594 = score(doc=910,freq=4.0), product of:
0.20667124 = queryWeight, product of:
3.9746525 = idf(docFreq=2257, maxDocs=44218)
0.05199731 = queryNorm
0.37262368 = fieldWeight in 910, product of:
2.0 = tf(freq=4.0), with freq of:
4.0 = termFreq=4.0
3.9746525 = idf(docFreq=2257, maxDocs=44218)
0.046875 = fieldNorm(doc=910)
0.5 = coord(1/2)
0.5 = coord(1/2)
- Abstract
- This paper compares 14 information retrieval metrics based on graded relevance, together with 10 traditional metrics based on binary relevance, in terms of stability, sensitivity and resemblance of system rankings. More specifically, we compare these metrics using the Buckley/Voorhees stability method, the Voorhees/Buckley swap method and Kendall's rank correlation, with three data sets comprising test collections and submitted runs from NTCIR. Our experiments show that (Average) Normalised Discounted Cumulative Gain at document cut-off l are the best among the rank-based graded-relevance metrics, provided that l is large. On the other hand, if one requires a recall-based graded-relevance metric that is highly correlated with Average Precision, then Q-measure is the best choice. Moreover, these best graded-relevance metrics are at least as stable and sensitive as Average Precision, and are fairly robust to the choice of gain values.
-
Zhao, L.; Wu, L.; Huang, X.: Using query expansion in graph-based approach for query-focused multi-document summarization (2009)
0.02
0.019252649 = product of:
0.038505297 = sum of:
0.038505297 = product of:
0.077010594 = sum of:
0.077010594 = weight(_text_:l in 2449) [ClassicSimilarity], result of:
0.077010594 = score(doc=2449,freq=4.0), product of:
0.20667124 = queryWeight, product of:
3.9746525 = idf(docFreq=2257, maxDocs=44218)
0.05199731 = queryNorm
0.37262368 = fieldWeight in 2449, product of:
2.0 = tf(freq=4.0), with freq of:
4.0 = termFreq=4.0
3.9746525 = idf(docFreq=2257, maxDocs=44218)
0.046875 = fieldNorm(doc=2449)
0.5 = coord(1/2)
0.5 = coord(1/2)
-
Cross-language information retrieval (1998)
0.02
0.01880698 = sum of:
0.007462248 = product of:
0.029848993 = sum of:
0.029848993 = weight(_text_:authors in 6299) [ClassicSimilarity], result of:
0.029848993 = score(doc=6299,freq=2.0), product of:
0.23704608 = queryWeight, product of:
4.558814 = idf(docFreq=1258, maxDocs=44218)
0.05199731 = queryNorm
0.12592064 = fieldWeight in 6299, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
4.558814 = idf(docFreq=1258, maxDocs=44218)
0.01953125 = fieldNorm(doc=6299)
0.25 = coord(1/4)
0.011344733 = product of:
0.022689465 = sum of:
0.022689465 = weight(_text_:l in 6299) [ClassicSimilarity], result of:
0.022689465 = score(doc=6299,freq=2.0), product of:
0.20667124 = queryWeight, product of:
3.9746525 = idf(docFreq=2257, maxDocs=44218)
0.05199731 = queryNorm
0.1097853 = fieldWeight in 6299, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.9746525 = idf(docFreq=2257, maxDocs=44218)
0.01953125 = fieldNorm(doc=6299)
0.5 = coord(1/2)
- Content
- Enthält die Beiträge: GREFENSTETTE, G.: The Problem of Cross-Language Information Retrieval; DAVIS, M.W.: On the Effective Use of Large Parallel Corpora in Cross-Language Text Retrieval; BALLESTEROS, L. u. W.B. CROFT: Statistical Methods for Cross-Language Information Retrieval; Distributed Cross-Lingual Information Retrieval; Automatic Cross-Language Information Retrieval Using Latent Semantic Indexing; EVANS, D.A. u.a.: Mapping Vocabularies Using Latent Semantics; PICCHI, E. u. C. PETERS: Cross-Language Information Retrieval: A System for Comparable Corpus Querying; YAMABANA, K. u.a.: A Language Conversion Front-End for Cross-Language Information Retrieval; GACHOT, D.A. u.a.: The Systran NLP Browser: An Application of Machine Translation Technology in Cross-Language Information Retrieval; HULL, D.: A Weighted Boolean Model for Cross-Language Text Retrieval; SHERIDAN, P. u.a. Building a Large Multilingual Test Collection from Comparable News Documents; OARD; D.W. u. B.J. DORR: Evaluating Cross-Language Text Filtering Effectiveness
- Footnote
- The retrieved output from a query including the phrase 'big rockets' may be, for instance, a sentence containing 'giant rocket' which is semantically ranked above 'military ocket'. David Hull (Xerox Research Centre, Grenoble) describes an implementation of a weighted Boolean model for Spanish-English CLIR. Users construct Boolean-type queries, weighting each term in the query, which is then translated by an on-line dictionary before being applied to the database. Comparisons with the performance of unweighted free-form queries ('vector space' models) proved encouraging. Two contributions consider the evaluation of CLIR systems. In order to by-pass the time-consuming and expensive process of assembling a standard collection of documents and of user queries against which the performance of an CLIR system is manually assessed, Páriac Sheridan et al (ETH Zurich) propose a method based on retrieving 'seed documents'. This involves identifying a unique document in a database (the 'seed document') and, for a number of queries, measuring how fast it is retrieved. The authors have also assembled a large database of multilingual news documents for testing purposes. By storing the (fairly short) documents in a structured form tagged with descriptor codes (e.g. for topic, country and area), the test suite is easily expanded while remaining consistent for the purposes of testing. Douglas Ouard and Bonne Dorr (University of Maryland) describe an evaluation methodology which appears to apply LSI techniques in order to filter and rank incoming documents designed for testing CLIR systems. The volume provides the reader an excellent overview of several projects in CLIR. It is well supported with references and is intended as a secondary text for researchers and practitioners. It highlights the need for a good, general tutorial introduction to the field."
-
Longshu, L.; Xia, Z.: On an aproximate fuzzy information retrieval agent (1998)
0.02
0.018151572 = product of:
0.036303144 = sum of:
0.036303144 = product of:
0.07260629 = sum of:
0.07260629 = weight(_text_:l in 3294) [ClassicSimilarity], result of:
0.07260629 = score(doc=3294,freq=2.0), product of:
0.20667124 = queryWeight, product of:
3.9746525 = idf(docFreq=2257, maxDocs=44218)
0.05199731 = queryNorm
0.35131297 = fieldWeight in 3294, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.9746525 = idf(docFreq=2257, maxDocs=44218)
0.0625 = fieldNorm(doc=3294)
0.5 = coord(1/2)
0.5 = coord(1/2)
-
Wartik, S.; Fox, E.; Heath, L.; Chen, Q.-F.: Hashing algorithms (1992)
0.02
0.018151572 = product of:
0.036303144 = sum of:
0.036303144 = product of:
0.07260629 = sum of:
0.07260629 = weight(_text_:l in 3510) [ClassicSimilarity], result of:
0.07260629 = score(doc=3510,freq=2.0), product of:
0.20667124 = queryWeight, product of:
3.9746525 = idf(docFreq=2257, maxDocs=44218)
0.05199731 = queryNorm
0.35131297 = fieldWeight in 3510, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.9746525 = idf(docFreq=2257, maxDocs=44218)
0.0625 = fieldNorm(doc=3510)
0.5 = coord(1/2)
0.5 = coord(1/2)
-
Maylein, L.; Langenstein, A.: Neues vom Relevanz-Ranking im HEIDI-Katalog der Universitätsbibliothek Heidelberg : Perspektiven für bibliothekarische Dienstleistungen (2013)
0.02
0.018151572 = product of:
0.036303144 = sum of:
0.036303144 = product of:
0.07260629 = sum of:
0.07260629 = weight(_text_:l in 775) [ClassicSimilarity], result of:
0.07260629 = score(doc=775,freq=2.0), product of:
0.20667124 = queryWeight, product of:
3.9746525 = idf(docFreq=2257, maxDocs=44218)
0.05199731 = queryNorm
0.35131297 = fieldWeight in 775, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.9746525 = idf(docFreq=2257, maxDocs=44218)
0.0625 = fieldNorm(doc=775)
0.5 = coord(1/2)
0.5 = coord(1/2)
-
Lee, D.L.; Ren, L.: Document ranking on weight-partitioned signature files (1996)
0.02
0.015882626 = product of:
0.031765252 = sum of:
0.031765252 = product of:
0.063530505 = sum of:
0.063530505 = weight(_text_:l in 2417) [ClassicSimilarity], result of:
0.063530505 = score(doc=2417,freq=2.0), product of:
0.20667124 = queryWeight, product of:
3.9746525 = idf(docFreq=2257, maxDocs=44218)
0.05199731 = queryNorm
0.30739886 = fieldWeight in 2417, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.9746525 = idf(docFreq=2257, maxDocs=44218)
0.0546875 = fieldNorm(doc=2417)
0.5 = coord(1/2)
0.5 = coord(1/2)
