Search (12 results, page 1 of 1)

0.04361048 = product of:
  0.08722096 = sum of:
    0.08722096 = sum of:
      0.04478263 = weight(_text_:retrieval in 2558) [ClassicSimilarity], result of:
        0.04478263 = score(doc=2558,freq=4.0), product of:
          0.15791564 = queryWeight, product of:
            3.024915 = idf(docFreq=5836, maxDocs=44218)
            0.052204985 = queryNorm
          0.2835858 = fieldWeight in 2558, product of:
            2.0 = tf(freq=4.0), with freq of:
              4.0 = termFreq=4.0
            3.024915 = idf(docFreq=5836, maxDocs=44218)
            0.046875 = fieldNorm(doc=2558)
      0.04243833 = weight(_text_:22 in 2558) [ClassicSimilarity], result of:
        0.04243833 = score(doc=2558,freq=2.0), product of:
          0.18281296 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.052204985 = queryNorm
          0.23214069 = fieldWeight in 2558, 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=2558)
  0.5 = coord(1/2)

Abstract

The paper shows that the present evaluation methods in information retrieval (basically recall R and precision P and in some cases fallout F ) lack universal comparability in the sense that their values depend on the generality of the IR problem. A solution is given by using all "parts" of the database, including the non-relevant documents and also the not-retrieved documents. It turns out that the solution is given by introducing the measure M being the fraction of the not-retrieved documents that are relevant (hence the "miss" measure). We prove that - independent of the IR problem or of the IR action - the quadruple (P,R,F,M) belongs to a universal IR surface, being the same for all IR-activities. This universality is then exploited by defining a new measure for evaluation in IR allowing for unbiased comparisons of all IR results. We also show that only using one, two or even three measures from the set {P,R,F,M} necessary leads to evaluation measures that are non-universal and hence not capable of comparing different IR situations.

Date

14. 8.2004 19:17:22

0.021110734 = product of:
  0.042221468 = sum of:
    0.042221468 = product of:
      0.084442936 = sum of:
        0.084442936 = weight(_text_:retrieval in 3267) [ClassicSimilarity], result of:
          0.084442936 = score(doc=3267,freq=8.0), product of:
            0.15791564 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.052204985 = queryNorm
            0.5347345 = fieldWeight in 3267, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.0625 = fieldNorm(doc=3267)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Topologies for information retrieval systems are generated by certain subsets, called retrievals. Shows how recall and precision can be expressed using only retrievals. Investigates different types of retrieval systems: both threshold systems and close match systems and both optimal and non optimal retrieval. Highlights the relation with the hypergeometric and some non-standard distributions

0.020652205 = product of:
  0.04130441 = sum of:
    0.04130441 = product of:
      0.08260882 = sum of:
        0.08260882 = weight(_text_:retrieval in 2157) [ClassicSimilarity], result of:
          0.08260882 = score(doc=2157,freq=10.0), product of:
            0.15791564 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.052204985 = queryNorm
            0.5231199 = fieldWeight in 2157, product of:
              3.1622777 = tf(freq=10.0), with freq of:
                10.0 = termFreq=10.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.0546875 = fieldNorm(doc=2157)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Let (DS, DQ, sim) be a retrieval system consisting of a document space DS, a query space QS, and a function sim, expressing the similarity between a document and a query. Following D.M. Everett and S.C. Cater (1992), we introduce topologies on the document space. These topologies are generated by the similarity function sim and the query space QS. 3 topologies will be studied: the retrieval topology, the similarity topology and the (pseudo-)metric one. It is shown that the retrieval topology is the coarsest of the three, while the (pseudo-)metric is the strongest. These 3 topologies are generally different, reflecting distinct topological aspects of information retrieval. We present necessary and sufficient conditions for these topological aspects to be equal

0.018659428 = product of:
  0.037318856 = sum of:
    0.037318856 = product of:
      0.07463771 = sum of:
        0.07463771 = weight(_text_:retrieval in 2067) [ClassicSimilarity], result of:
          0.07463771 = score(doc=2067,freq=16.0), product of:
            0.15791564 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.052204985 = queryNorm
            0.47264296 = fieldWeight in 2067, product of:
              4.0 = tf(freq=16.0), with freq of:
                16.0 = termFreq=16.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2067)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

In this paper, for the first time, we present global curves for the measures precision, recall, fallout and miss in function of the number of retrieved documents. Different curves apply for different retrieved systems, for which we give exact definitions in terms of a retrieval density function: perverse retrieval, perfect retrieval, random retrieval, normal retrieval, hereby extending results of Buckland and Gey and of Egghe in the following sense: mathematically more advanced methods yield a better insight into these curves, more types of retrieval are considered and, very importantly, the theory is developed for the "complete" set of measures: precision, recall, fallout and miss. Next we study the interrelationships between precision, recall, fallout and miss in these different types of retrieval, hereby again extending results of Buckland and Gey (incl. a correction) and of Egghe. In the case of normal retrieval we prove that precision in function of recall and recall in function of miss is a concavely decreasing relationship while recall in function of fallout is a concavely increasing relationship. We also show, by producing examples, that the relationships between fallout and precision, miss and precision and miss and fallout are not always convex or concave.

0.017682636 = product of:
  0.035365272 = sum of:
    0.035365272 = product of:
      0.070730545 = sum of:
        0.070730545 = weight(_text_:22 in 4992) [ClassicSimilarity], result of:
          0.070730545 = score(doc=4992,freq=2.0), product of:
            0.18281296 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.052204985 = queryNorm
            0.38690117 = fieldWeight in 4992, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.078125 = fieldNorm(doc=4992)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Date

14. 2.2012 12:53:22

0.015997129 = product of:
  0.031994257 = sum of:
    0.031994257 = product of:
      0.063988514 = sum of:
        0.063988514 = weight(_text_:retrieval in 2531) [ClassicSimilarity], result of:
          0.063988514 = score(doc=2531,freq=6.0), product of:
            0.15791564 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.052204985 = queryNorm
            0.40520695 = fieldWeight in 2531, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.0546875 = fieldNorm(doc=2531)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

It is shown that vector information retrieval (IR) and general fuzzy IR uses two types of fuzzy set operations: the original "Zadeh min-max operations" and the so-called "probabilistic sum and algebraic product operations". The universal IR surface, valid for classical 0-1 IR (i.e. where ordinary sets are used) and used in IR evaluation, is extended to and reproved for vector IR, using the probabilistic sum and algebraic product model. We also show (by counterexample) that, using the "Zadeh min-max" fuzzy model, yields a breakdown of this IR surface.

0.014927543 = product of:
  0.029855086 = sum of:
    0.029855086 = product of:
      0.05971017 = sum of:
        0.05971017 = weight(_text_:retrieval in 647) [ClassicSimilarity], result of:
          0.05971017 = score(doc=647,freq=4.0), product of:
            0.15791564 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.052204985 = queryNorm
            0.37811437 = fieldWeight in 647, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.0625 = fieldNorm(doc=647)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Asserts that duality is an important topic in informetrics, especially in connection with the classical informetric laws. Yet this concept is less studied in information retrieval. It deals with the unification or symmetry between queries and documents, search formulation versus indexing, and relevant versus retrieved documents. Elaborates these ideas and highlights the connection with the hypergeometric distribution

0.0130616 = product of:
  0.0261232 = sum of:
    0.0261232 = product of:
      0.0522464 = sum of:
        0.0522464 = weight(_text_:retrieval in 1759) [ClassicSimilarity], result of:
          0.0522464 = score(doc=1759,freq=4.0), product of:
            0.15791564 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.052204985 = queryNorm
            0.33085006 = fieldWeight in 1759, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.0546875 = fieldNorm(doc=1759)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Purpose - The authors exploit the analogy between journal peer review and information retrieval in order to quantify some imperfections of journal peer review. Design/methodology/approach - The authors define fallout rate and missing rate in order to describe quantitatively the weak papers that were accepted and the strong papers that were missed, respectively. To assess the quality of manuscripts the authors use bibliometric measures. Findings - Fallout rate and missing rate are put in relation with the hitting rate and success rate. Conclusions are drawn on what fraction of weak papers will be accepted in order to have a certain fraction of strong accepted papers. Originality/value - The paper illustrates that these curves are new in peer review research when interpreted in the information retrieval terminology.

0.010609582 = product of:
  0.021219164 = sum of:
    0.021219164 = product of:
      0.04243833 = sum of:
        0.04243833 = weight(_text_:22 in 7659) [ClassicSimilarity], result of:
          0.04243833 = score(doc=7659,freq=2.0), product of:
            0.18281296 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.052204985 = queryNorm
            0.23214069 = fieldWeight in 7659, 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=7659)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Source

Journal of information science. 22(1996) no.3, S.165-170

0.007916525 = product of:
  0.01583305 = sum of:
    0.01583305 = product of:
      0.0316661 = sum of:
        0.0316661 = weight(_text_:retrieval in 2011) [ClassicSimilarity], result of:
          0.0316661 = score(doc=2011,freq=2.0), product of:
            0.15791564 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.052204985 = queryNorm
            0.20052543 = fieldWeight in 2011, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.046875 = fieldNorm(doc=2011)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

In an earlier paper [Egghe, L. (2004). A universal method of information retrieval evaluation: the "missing" link M and the universal IR surface. Information Processing and Management, 40, 21-30] we showed that, given an IR system, and if P denotes precision, R recall, F fallout and M miss (re-introduced in the paper mentioned above), we have the following relationship between P, R, F and M: P/(1-P)*(1-R)/R*F/(1-F)*(1-M)/M = 1. In this paper we prove the (more difficult) converse: given any four rational numbers in the interval ]0, 1[ satisfying the above equation, then there exists an IR system such that these four numbers (in any order) are the precision, recall, fallout and miss of this IR system. As a consequence we show that any three rational numbers in ]0, 1[ represent any three measures taken from precision, recall, fallout and miss of a certain IR system. We also show that this result is also true for two numbers instead of three.

0.0065971045 = product of:
  0.013194209 = sum of:
    0.013194209 = product of:
      0.026388418 = sum of:
        0.026388418 = weight(_text_:retrieval in 1608) [ClassicSimilarity], result of:
          0.026388418 = score(doc=1608,freq=2.0), product of:
            0.15791564 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.052204985 = queryNorm
            0.16710453 = fieldWeight in 1608, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1608)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Type/Token-Taken informetrics is a new part of informetrics that studies the use of items rather than the items itself. Here, items are the objects that are produced by the sources (e.g., journals producing articles, authors producing papers, etc.). In linguistics a source is also called a type (e.g., a word), and an item a token (e.g., the use of words in texts). In informetrics, types that occur often, for example, in a database will also be requested often, for example, in information retrieval. The relative use of these occurrences will be higher than their relative occurrences itself; hence, the name Type/ Token-Taken informetrics. This article studies the frequency distribution of Type/Token-Taken informetrics, starting from the one of Type/Token informetrics (i.e., source-item relationships). We are also studying the average number my* of item uses in Type/Token-Taken informetrics and compare this with the classical average number my in Type/Token informetrics. We show that my* >= my always, and that my* is an increasing function of my. A method is presented to actually calculate my* from my, and a given a, which is the exponent in Lotka's frequency distribution of Type/Token informetrics. We leave open the problem of developing non-Lotkaian Type/TokenTaken informetrics.

0.0065971045 = product of:
  0.013194209 = sum of:
    0.013194209 = product of:
      0.026388418 = sum of:
        0.026388418 = weight(_text_:retrieval in 271) [ClassicSimilarity], result of:
          0.026388418 = score(doc=271,freq=2.0), product of:
            0.15791564 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.052204985 = queryNorm
            0.16710453 = fieldWeight in 271, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.0390625 = fieldNorm(doc=271)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Herdan's law in linguistics and Heaps' law in information retrieval are different formulations of the same phenomenon. Stated briefly and in linguistic terms they state that vocabularies' sizes are concave increasing power laws of texts' sizes. This study investigates these laws from a purely mathematical and informetric point of view. A general informetric argument shows that the problem of proving these laws is, in fact, ill-posed. Using the more general terminology of sources and items, the author shows by presenting exact formulas from Lotkaian informetrics that the total number T of sources is not only a function of the total number A of items, but is also a function of several parameters (e.g., the parameters occurring in Lotka's law). Consequently, it is shown that a fixed T(or A) value can lead to different possible A (respectively, T) values. Limiting the T(A)-variability to increasing samples (e.g., in a text as done in linguistics) the author then shows, in a purely mathematical way, that for large sample sizes T~ A**phi, where phi is a constant, phi < 1 but close to 1, hence roughly, Heaps' or Herdan's law can be proved without using any linguistic or informetric argument. The author also shows that for smaller samples, a is not a constant but essentially decreases as confirmed by practical examples. Finally, an exact informetric argument on random sampling in the items shows that, in most cases, T= T(A) is a concavely increasing function, in accordance with practical examples.