Search (138 results, page 2 of 7)

  • × language_ss:"e"
  • × theme_ss:"Retrievalalgorithmen"
  • × year_i:[2000 TO 2010}
  1. Lee, C.; Lee, G.G.: Probabilistic information retrieval model for a dependence structured indexing system (2005) 0.01 
    0.008874074 = product of:
  0.022185184 = sum of:
    0.012614433 = weight(_text_:a in 1004) [ClassicSimilarity], result of:
      0.012614433 = score(doc=1004,freq=14.0), product of:
        0.053464882 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.046368346 = queryNorm
        0.23593865 = fieldWeight in 1004, product of:
          3.7416575 = tf(freq=14.0), with freq of:
            14.0 = termFreq=14.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.0546875 = fieldNorm(doc=1004)
    0.009570752 = product of:
      0.019141505 = sum of:
        0.019141505 = weight(_text_:information in 1004) [ClassicSimilarity], result of:
          0.019141505 = score(doc=1004,freq=6.0), product of:
            0.08139861 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.046368346 = queryNorm
            0.23515764 = fieldWeight in 1004, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.0546875 = fieldNorm(doc=1004)
      0.5 = coord(1/2)
  0.4 = coord(2/5)
    Abstract
    Most previous information retrieval (IR) models assume that terms of queries and documents are statistically independent from each other. However, conditional independence assumption is obviously and openly understood to be wrong, so we present a new method of incorporating term dependence into a probabilistic retrieval model by adapting a dependency structured indexing system using a dependency parse tree and Chow Expansion to compensate the weakness of the assumption. In this paper, we describe a theoretic process to apply the Chow Expansion to the general probabilistic models and the state-of-the-art 2-Poisson model. Through experiments on document collections in English and Korean, we demonstrate that the incorporation of term dependences using Chow Expansion contributes to the improvement of performance in probabilistic IR systems.
    Source
    Information processing and management. 41(2005) no.2, S.161-176
    Type
    a
  2. Torra, V.; Miyamoto, S.; Lanau, S.: Exploration of textual document archives using a fuzzy hierarchical clustering algorithm in the GAMBAL system (2005) 0.01 
    0.008874074 = product of:
  0.022185184 = sum of:
    0.012614433 = weight(_text_:a in 1028) [ClassicSimilarity], result of:
      0.012614433 = score(doc=1028,freq=14.0), product of:
        0.053464882 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.046368346 = queryNorm
        0.23593865 = fieldWeight in 1028, product of:
          3.7416575 = tf(freq=14.0), with freq of:
            14.0 = termFreq=14.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.0546875 = fieldNorm(doc=1028)
    0.009570752 = product of:
      0.019141505 = sum of:
        0.019141505 = weight(_text_:information in 1028) [ClassicSimilarity], result of:
          0.019141505 = score(doc=1028,freq=6.0), product of:
            0.08139861 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.046368346 = queryNorm
            0.23515764 = fieldWeight in 1028, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.0546875 = fieldNorm(doc=1028)
      0.5 = coord(1/2)
  0.4 = coord(2/5)
    Abstract
    The Internet, together with the large amount of textual information available in document archives, has increased the relevance of information retrieval related tools. In this work we present an extension of the Gambal system for clustering and visualization of documents based on fuzzy clustering techniques. The tool allows to structure the set of documents in a hierarchical way (using a fuzzy hierarchical structure) and represent this structure in a graphical interface (a 3D sphere) over which the user can navigate. Gambal allows the analysis of the documents and the computation of their similarity not only on the basis of the syntactic similarity between words but also based on a dictionary (Wordnet 1.7) and latent semantics analysis.
    Source
    Information processing and management. 41(2005) no.3, S.587-598
    Type
    a
  3. Khoo, C.S.G.; Wan, K.-W.: ¬A simple relevancy-ranking strategy for an interface to Boolean OPACs (2004) 0.01 
    0.008870189 = product of:
  0.022175472 = sum of:
    0.011181499 = weight(_text_:a in 2509) [ClassicSimilarity], result of:
      0.011181499 = score(doc=2509,freq=44.0), product of:
        0.053464882 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.046368346 = queryNorm
        0.20913726 = fieldWeight in 2509, product of:
          6.6332498 = tf(freq=44.0), with freq of:
            44.0 = termFreq=44.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.02734375 = fieldNorm(doc=2509)
    0.010993974 = product of:
      0.021987949 = sum of:
        0.021987949 = weight(_text_:22 in 2509) [ClassicSimilarity], result of:
          0.021987949 = score(doc=2509,freq=2.0), product of:
            0.16237405 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046368346 = 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)
  0.4 = coord(2/5)
    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
    Type
    a
  4. Hubert, G.; Mothe, J.: ¬An adaptable search engine for multimodal information retrieval (2009) 0.01 
    0.008734339 = product of:
  0.021835847 = sum of:
    0.010897844 = weight(_text_:a in 2951) [ClassicSimilarity], result of:
      0.010897844 = score(doc=2951,freq=8.0), product of:
        0.053464882 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.046368346 = queryNorm
        0.20383182 = fieldWeight in 2951, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.0625 = fieldNorm(doc=2951)
    0.010938003 = product of:
      0.021876005 = sum of:
        0.021876005 = weight(_text_:information in 2951) [ClassicSimilarity], result of:
          0.021876005 = score(doc=2951,freq=6.0), product of:
            0.08139861 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.046368346 = queryNorm
            0.2687516 = fieldWeight in 2951, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.0625 = fieldNorm(doc=2951)
      0.5 = coord(1/2)
  0.4 = coord(2/5)
    Abstract
    This article describes an information retrieval approach according to the two different search modes that exist: browsing an ontology (via categories) or defining a query in free language (via keywords). Various proposals offer approaches adapted to one of these two modes. We present a proposal leading to a system allowing the integration of both modes using the same search engine. This engine is adapted according to each possible search mode.
    Source
    Journal of the American Society for Information Science and Technology. 60(2009) no.8, S.1625-1634
    Type
    a
  5. Herrera-Viedma, E.; Cordón, O.; Herrera, J.C.; Luqe, M.: ¬An IRS based on multi-granular lnguistic information (2003) 0.01 
    0.008561219 = product of:
  0.021403046 = sum of:
    0.010812371 = weight(_text_:a in 2740) [ClassicSimilarity], result of:
      0.010812371 = score(doc=2740,freq=14.0), product of:
        0.053464882 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.046368346 = queryNorm
        0.20223314 = fieldWeight in 2740, product of:
          3.7416575 = tf(freq=14.0), with freq of:
            14.0 = termFreq=14.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.046875 = fieldNorm(doc=2740)
    0.010590675 = product of:
      0.02118135 = sum of:
        0.02118135 = weight(_text_:information in 2740) [ClassicSimilarity], result of:
          0.02118135 = score(doc=2740,freq=10.0), product of:
            0.08139861 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.046368346 = queryNorm
            0.2602176 = fieldWeight in 2740, product of:
              3.1622777 = tf(freq=10.0), with freq of:
                10.0 = termFreq=10.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.046875 = fieldNorm(doc=2740)
      0.5 = coord(1/2)
  0.4 = coord(2/5)
    Abstract
    An information retrieval system (IRS) based on fuzzy multi-granular linguistic information is proposed. The system has an evaluation method to process multi-granular linguistic information, in such a way that the inputs to the IRS are represented in a different linguistic domain than the outputs. The system accepts Boolean queries whose terms are weighted by means of the ordinal linguistic values represented by the linguistic variable "Importance" assessed an a label set S. The system evaluates the weighted queries according to a threshold semantic and obtains the linguistic retrieval status values (RSV) of documents represented by a linguistic variable "Relevance" expressed in a different label set S'. The advantage of this linguistic IRS with respect to others is that the use of the multi-granular linguistic information facilitates and improves the IRS-user interaction
    Type
    a
  6. Silveira, M.; Ribeiro-Neto, B.: Concept-based ranking : a case study in the juridical domain (2004) 0.01 
    0.008412599 = product of:
  0.021031497 = sum of:
    0.01155891 = weight(_text_:a in 2339) [ClassicSimilarity], result of:
      0.01155891 = score(doc=2339,freq=4.0), product of:
        0.053464882 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.046368346 = queryNorm
        0.2161963 = fieldWeight in 2339, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.09375 = fieldNorm(doc=2339)
    0.009472587 = product of:
      0.018945174 = sum of:
        0.018945174 = weight(_text_:information in 2339) [ClassicSimilarity], result of:
          0.018945174 = score(doc=2339,freq=2.0), product of:
            0.08139861 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.046368346 = queryNorm
            0.23274569 = fieldWeight in 2339, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.09375 = fieldNorm(doc=2339)
      0.5 = coord(1/2)
  0.4 = coord(2/5)
    Source
    Information processing and management. 40(2004) no.5, S.791-806
    Type
    a
  7. Liu, A.; Zou, Q.; Chu, W.W.: Configurable indexing and ranking for XML information retrieval (2004) 0.01 
    0.008318391 = product of:
  0.020795977 = sum of:
    0.009632425 = weight(_text_:a in 4114) [ClassicSimilarity], result of:
      0.009632425 = score(doc=4114,freq=4.0), product of:
        0.053464882 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.046368346 = queryNorm
        0.18016359 = fieldWeight in 4114, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.078125 = fieldNorm(doc=4114)
    0.011163551 = product of:
      0.022327103 = sum of:
        0.022327103 = weight(_text_:information in 4114) [ClassicSimilarity], result of:
          0.022327103 = score(doc=4114,freq=4.0), product of:
            0.08139861 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.046368346 = queryNorm
            0.27429342 = fieldWeight in 4114, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.078125 = fieldNorm(doc=4114)
      0.5 = coord(1/2)
  0.4 = coord(2/5)
    Source
    SIGIR'04: Proceedings of the 27th Annual International ACM-SIGIR Conference an Research and Development in Information Retrieval. Ed.: K. Järvelin, u.a
    Type
    a
  8. Yu, K.; Tresp, V.; Yu, S.: ¬A nonparametric hierarchical Bayesian framework for information filtering (2004) 0.01 
    0.008318391 = product of:
  0.020795977 = sum of:
    0.009632425 = weight(_text_:a in 4117) [ClassicSimilarity], result of:
      0.009632425 = score(doc=4117,freq=4.0), product of:
        0.053464882 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.046368346 = queryNorm
        0.18016359 = fieldWeight in 4117, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.078125 = fieldNorm(doc=4117)
    0.011163551 = product of:
      0.022327103 = sum of:
        0.022327103 = weight(_text_:information in 4117) [ClassicSimilarity], result of:
          0.022327103 = score(doc=4117,freq=4.0), product of:
            0.08139861 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.046368346 = queryNorm
            0.27429342 = fieldWeight in 4117, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.078125 = fieldNorm(doc=4117)
      0.5 = coord(1/2)
  0.4 = coord(2/5)
    Source
    SIGIR'04: Proceedings of the 27th Annual International ACM-SIGIR Conference an Research and Development in Information Retrieval. Ed.: K. Järvelin, u.a
    Type
    a
  9. Lavrenko, V.: ¬A generative theory of relevance (2009) 0.01 
    0.00826371 = product of:
  0.020659275 = sum of:
    0.01021673 = weight(_text_:a in 3306) [ClassicSimilarity], result of:
      0.01021673 = score(doc=3306,freq=18.0), product of:
        0.053464882 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.046368346 = queryNorm
        0.19109234 = fieldWeight in 3306, product of:
          4.2426405 = tf(freq=18.0), with freq of:
            18.0 = termFreq=18.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3306)
    0.010442546 = product of:
      0.020885091 = sum of:
        0.020885091 = weight(_text_:information in 3306) [ClassicSimilarity], result of:
          0.020885091 = score(doc=3306,freq=14.0), product of:
            0.08139861 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.046368346 = queryNorm
            0.256578 = fieldWeight in 3306, product of:
              3.7416575 = tf(freq=14.0), with freq of:
                14.0 = termFreq=14.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3306)
      0.5 = coord(1/2)
  0.4 = coord(2/5)
    Abstract
    A modern information retrieval system must have the capability to find, organize and present very different manifestations of information - such as text, pictures, videos or database records - any of which may be of relevance to the user. However, the concept of relevance, while seemingly intuitive, is actually hard to define, and it's even harder to model in a formal way. Lavrenko does not attempt to bring forth a new definition of relevance, nor provide arguments as to why any particular definition might be theoretically superior or more complete. Instead, he takes a widely accepted, albeit somewhat conservative definition, makes several assumptions, and from them develops a new probabilistic model that explicitly captures that notion of relevance. With this book, he makes two major contributions to the field of information retrieval: first, a new way to look at topical relevance, complementing the two dominant models, i.e., the classical probabilistic model and the language modeling approach, and which explicitly combines documents, queries, and relevance in a single formalism; second, a new method for modeling exchangeable sequences of discrete random variables which does not make any structural assumptions about the data and which can also handle rare events. Thus his book is of major interest to researchers and graduate students in information retrieval who specialize in relevance modeling, ranking algorithms, and language modeling.
    RSWK
    Relevanz-Feedback / Information Retrieval
    Series
    The information retrieval series ; 26
    Subject
    Relevanz-Feedback / Information Retrieval
  10. Daniowicz, C.; Baliski, J.: Document ranking based upon Markov chains (2001) 0.01 
    0.008234787 = product of:
  0.020586967 = sum of:
    0.009535614 = weight(_text_:a in 5388) [ClassicSimilarity], result of:
      0.009535614 = score(doc=5388,freq=2.0), product of:
        0.053464882 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.046368346 = queryNorm
        0.17835285 = fieldWeight in 5388, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.109375 = fieldNorm(doc=5388)
    0.011051352 = product of:
      0.022102704 = sum of:
        0.022102704 = weight(_text_:information in 5388) [ClassicSimilarity], result of:
          0.022102704 = score(doc=5388,freq=2.0), product of:
            0.08139861 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.046368346 = queryNorm
            0.27153665 = fieldWeight in 5388, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.109375 = fieldNorm(doc=5388)
      0.5 = coord(1/2)
  0.4 = coord(2/5)
    Source
    Information processing and management. 37(2001) no.4, S.623-637
    Type
    a
  11. Horng, J.T.; Yeh, C.C.: Applying genetic algorithms to query optimization in document retrieval (2000) 0.01 
    0.008234787 = product of:
  0.020586967 = sum of:
    0.009535614 = weight(_text_:a in 3045) [ClassicSimilarity], result of:
      0.009535614 = score(doc=3045,freq=2.0), product of:
        0.053464882 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.046368346 = queryNorm
        0.17835285 = fieldWeight in 3045, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.109375 = fieldNorm(doc=3045)
    0.011051352 = product of:
      0.022102704 = sum of:
        0.022102704 = weight(_text_:information in 3045) [ClassicSimilarity], result of:
          0.022102704 = score(doc=3045,freq=2.0), product of:
            0.08139861 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.046368346 = queryNorm
            0.27153665 = fieldWeight in 3045, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.109375 = fieldNorm(doc=3045)
      0.5 = coord(1/2)
  0.4 = coord(2/5)
    Source
    Information processing and management. 36(2000) no.5, S.737-759
    Type
    a
  12. Niemi, T.; Junkkari, M.; Järvelin, K.; Viita, S.: Advanced query language for manipulating complex entities (2004) 0.01 
    0.008234787 = product of:
  0.020586967 = sum of:
    0.009535614 = weight(_text_:a in 4218) [ClassicSimilarity], result of:
      0.009535614 = score(doc=4218,freq=2.0), product of:
        0.053464882 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.046368346 = queryNorm
        0.17835285 = fieldWeight in 4218, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.109375 = fieldNorm(doc=4218)
    0.011051352 = product of:
      0.022102704 = sum of:
        0.022102704 = weight(_text_:information in 4218) [ClassicSimilarity], result of:
          0.022102704 = score(doc=4218,freq=2.0), product of:
            0.08139861 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.046368346 = queryNorm
            0.27153665 = fieldWeight in 4218, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.109375 = fieldNorm(doc=4218)
      0.5 = coord(1/2)
  0.4 = coord(2/5)
    Source
    Information processing and management. 40(2004) no.6, S.869-
    Type
    a
  13. Clarke, C.L.A.; Cormack, G.V.; Tudhope, E.A.: Relevance ranking for one to three term queries (2000) 0.01 
    0.008234787 = product of:
  0.020586967 = sum of:
    0.009535614 = weight(_text_:a in 437) [ClassicSimilarity], result of:
      0.009535614 = score(doc=437,freq=2.0), product of:
        0.053464882 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.046368346 = queryNorm
        0.17835285 = fieldWeight in 437, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.109375 = fieldNorm(doc=437)
    0.011051352 = product of:
      0.022102704 = sum of:
        0.022102704 = weight(_text_:information in 437) [ClassicSimilarity], result of:
          0.022102704 = score(doc=437,freq=2.0), product of:
            0.08139861 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.046368346 = queryNorm
            0.27153665 = fieldWeight in 437, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.109375 = fieldNorm(doc=437)
      0.5 = coord(1/2)
  0.4 = coord(2/5)
    Source
    Information processing and management. 36(2000) no.2, S.291-311
    Type
    a
  14. Chung, Y.M.; Lee, J.Y.: Optimization of some factors affecting the performance of query expansion (2004) 0.01 
    0.008234787 = product of:
  0.020586967 = sum of:
    0.009535614 = weight(_text_:a in 2537) [ClassicSimilarity], result of:
      0.009535614 = score(doc=2537,freq=2.0), product of:
        0.053464882 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.046368346 = queryNorm
        0.17835285 = fieldWeight in 2537, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.109375 = fieldNorm(doc=2537)
    0.011051352 = product of:
      0.022102704 = sum of:
        0.022102704 = weight(_text_:information in 2537) [ClassicSimilarity], result of:
          0.022102704 = score(doc=2537,freq=2.0), product of:
            0.08139861 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.046368346 = queryNorm
            0.27153665 = fieldWeight in 2537, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.109375 = fieldNorm(doc=2537)
      0.5 = coord(1/2)
  0.4 = coord(2/5)
    Source
    Information processing and management. 40(2004) no.6, S.891-
    Type
    a
  15. Crestani, F.: Combination of similarity measures for effective spoken document retrieval (2003) 0.01 
    0.008234787 = product of:
  0.020586967 = sum of:
    0.009535614 = weight(_text_:a in 4690) [ClassicSimilarity], result of:
      0.009535614 = score(doc=4690,freq=2.0), product of:
        0.053464882 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.046368346 = queryNorm
        0.17835285 = fieldWeight in 4690, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.109375 = fieldNorm(doc=4690)
    0.011051352 = product of:
      0.022102704 = sum of:
        0.022102704 = weight(_text_:information in 4690) [ClassicSimilarity], result of:
          0.022102704 = score(doc=4690,freq=2.0), product of:
            0.08139861 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.046368346 = queryNorm
            0.27153665 = fieldWeight in 4690, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.109375 = fieldNorm(doc=4690)
      0.5 = coord(1/2)
  0.4 = coord(2/5)
    Source
    Journal of information science. 29(2003) no.2, S.87-96
    Type
    a
  16. García Cumbreras, M.A.; Perea-Ortega, J.M.; García Vega, M.; Ureña López, L.A.: Information retrieval with geographical references : relevant documents filtering vs. query expansion (2009) 0.01 
    0.008234787 = product of:
  0.020586967 = sum of:
    0.009535614 = weight(_text_:a in 4222) [ClassicSimilarity], result of:
      0.009535614 = score(doc=4222,freq=8.0), product of:
        0.053464882 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.046368346 = queryNorm
        0.17835285 = fieldWeight in 4222, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.0546875 = fieldNorm(doc=4222)
    0.011051352 = product of:
      0.022102704 = sum of:
        0.022102704 = weight(_text_:information in 4222) [ClassicSimilarity], result of:
          0.022102704 = score(doc=4222,freq=8.0), product of:
            0.08139861 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.046368346 = queryNorm
            0.27153665 = fieldWeight in 4222, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.0546875 = fieldNorm(doc=4222)
      0.5 = coord(1/2)
  0.4 = coord(2/5)
    Abstract
    This is a thorough analysis of two techniques applied to Geographic Information Retrieval (GIR). Previous studies have researched the application of query expansion to improve the selection process of information retrieval systems. This paper emphasizes the effectiveness of the filtering of relevant documents applied to a GIR system, instead of query expansion. Based on the CLEF (Cross Language Evaluation Forum) framework available, several experiments have been run. Some based on query expansion, some on the filtering of relevant documents. The results show that filtering works better in a GIR environment, because relevant documents are not reordered in the final list.
    Source
    Information processing and management. 45(2009) no.5, S.605-614
    Type
    a
  17. Ponte, J.M.: Language models for relevance feedback (2000) 0.01 
    0.008113983 = product of:
  0.020284958 = sum of:
    0.010812371 = weight(_text_:a in 35) [ClassicSimilarity], result of:
      0.010812371 = score(doc=35,freq=14.0), product of:
        0.053464882 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.046368346 = queryNorm
        0.20223314 = fieldWeight in 35, product of:
          3.7416575 = tf(freq=14.0), with freq of:
            14.0 = termFreq=14.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.046875 = fieldNorm(doc=35)
    0.009472587 = product of:
      0.018945174 = sum of:
        0.018945174 = weight(_text_:information in 35) [ClassicSimilarity], result of:
          0.018945174 = score(doc=35,freq=8.0), product of:
            0.08139861 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.046368346 = queryNorm
            0.23274569 = fieldWeight in 35, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.046875 = fieldNorm(doc=35)
      0.5 = coord(1/2)
  0.4 = coord(2/5)
    Abstract
    The language modeling approach to Information Retrieval (IR) is a conceptually simple model of IR originally developed by Ponte and Croft (1998). In this approach, the query is treated as a random event and documents are ranked according to the likelihood that the query would be generated via a language model estimated for each document. The intuition behind this approach is that users have a prototypical document in mind and will choose query terms accordingly. The intuitive appeal of this method is that inferences about the semantic content of documents do not need to be made resulting in a conceptually simple model. In this paper, techniques for relevance feedback and routing are derived from the language modeling approach in a straightforward manner and their effectiveness is demonstrated empirically. These experiments demonstrate further proof of concept for the language modeling approach to retrieval
    Series
    The Kluwer international series on information retrieval; 7
    Source
    Advances in information retrieval: Recent research from the Center for Intelligent Information Retrieval. Ed.: W.B. Croft
    Type
    a
  18. Rokaya, M.; Atlam, E.; Fuketa, M.; Dorji, T.C.; Aoe, J.-i.: Ranking of field association terms using Co-word analysis (2008) 0.01 
    0.008113983 = product of:
  0.020284958 = sum of:
    0.010812371 = weight(_text_:a in 2060) [ClassicSimilarity], result of:
      0.010812371 = score(doc=2060,freq=14.0), product of:
        0.053464882 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.046368346 = queryNorm
        0.20223314 = fieldWeight in 2060, product of:
          3.7416575 = tf(freq=14.0), with freq of:
            14.0 = termFreq=14.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.046875 = fieldNorm(doc=2060)
    0.009472587 = product of:
      0.018945174 = sum of:
        0.018945174 = weight(_text_:information in 2060) [ClassicSimilarity], result of:
          0.018945174 = score(doc=2060,freq=8.0), product of:
            0.08139861 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.046368346 = queryNorm
            0.23274569 = fieldWeight in 2060, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.046875 = fieldNorm(doc=2060)
      0.5 = coord(1/2)
  0.4 = coord(2/5)
    Abstract
    Information retrieval involves finding some desired information in a store of information or a database. In this paper, Co-word analysis will be used to achieve a ranking of a selected sample of FA terms. Based on this ranking a better arranging of search results can be achieved. Experimental results achieved using 41 MB of data (7660 documents) in the field of sports. The corpus was collected from CNN newspaper, sports field. This corpus was chosen to be distributed over 11 sub-fields of the field sports from the experimental results, the average precision increased by 18.3% after applying the proposed arranging scheme depending on the absolute frequency to count the terms weights, and the average precision increased by 17.2% after applying the proposed arranging scheme depending on a formula based on "TF*IDF" to count the terms weights.
    Source
    Information processing and management. 44(2008) no.2, S.738-755
    Type
    a
  19. Ding, Y.; Chowdhury, G.; Foo, S.: Organsising keywords in a Web search environment : a methodology based on co-word analysis (2000) 0.01 
    0.007891519 = product of:
  0.019728797 = sum of:
    0.009138121 = weight(_text_:a in 105) [ClassicSimilarity], result of:
      0.009138121 = score(doc=105,freq=10.0), product of:
        0.053464882 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.046368346 = queryNorm
        0.1709182 = fieldWeight in 105, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.046875 = fieldNorm(doc=105)
    0.010590675 = product of:
      0.02118135 = sum of:
        0.02118135 = weight(_text_:information in 105) [ClassicSimilarity], result of:
          0.02118135 = score(doc=105,freq=10.0), product of:
            0.08139861 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.046368346 = queryNorm
            0.2602176 = fieldWeight in 105, product of:
              3.1622777 = tf(freq=10.0), with freq of:
                10.0 = termFreq=10.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.046875 = fieldNorm(doc=105)
      0.5 = coord(1/2)
  0.4 = coord(2/5)
    Abstract
    The rapid development of the Internet and World Wide Web has caused some critical problem for information retrieval. Researchers have made several attempts to solve these problems. Thesauri and subject heading lists as traditional information retrieval tools have been criticised for their efficiency to tackle these newly emerging problems. This paper proposes an information retrieval tool generated by cocitation analysis, comprising keyword clusters with relationships based on the co-occurrences of keywords in the literature. Such a tool can play the role of an associative thesaurus that can provide information about the keywords in a domain that might be useful for information searching and query expansion
    Type
    a
  20. Calegari, S.; Sanchez, E.: Object-fuzzy concept network : an enrichment of ontologies in semantic information retrieval (2008) 0.01 
    0.007831501 = product of:
  0.019578751 = sum of:
    0.0127425 = weight(_text_:a in 2393) [ClassicSimilarity], result of:
      0.0127425 = score(doc=2393,freq=28.0), product of:
        0.053464882 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.046368346 = queryNorm
        0.23833402 = fieldWeight in 2393, product of:
          5.2915025 = tf(freq=28.0), with freq of:
            28.0 = termFreq=28.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2393)
    0.006836252 = product of:
      0.013672504 = sum of:
        0.013672504 = weight(_text_:information in 2393) [ClassicSimilarity], result of:
          0.013672504 = score(doc=2393,freq=6.0), product of:
            0.08139861 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.046368346 = queryNorm
            0.16796975 = fieldWeight in 2393, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2393)
      0.5 = coord(1/2)
  0.4 = coord(2/5)
    Abstract
    This article shows how a fuzzy ontology-based approach can improve semantic documents retrieval. After formally defining a fuzzy ontology and a fuzzy knowledge base, a special type of new fuzzy relationship called (semantic) correlation, which links the concepts or entities in a fuzzy ontology, is discussed. These correlations, first assigned by experts, are updated after querying or when a document has been inserted into a database. Moreover, in order to define a dynamic knowledge of a domain adapting itself to the context, it is shown how to handle a tradeoff between the correct definition of an object, taken in the ontology structure, and the actual meaning assigned by individuals. The notion of a fuzzy concept network is extended, incorporating database objects so that entities and documents can similarly be represented in the network. Information retrieval (IR) algorithm, using an object-fuzzy concept network (O-FCN), is introduced and described. This algorithm allows us to derive a unique path among the entities involved in the query to obtain maxima semantic associations in the knowledge domain. Finally, the study has been validated by querying a database using fuzzy recall, fuzzy precision, and coefficient variant measures in the crisp and fuzzy cases.
    Source
    Journal of the American Society for Information Science and Technology. 59(2008) no.13, S.2171-2185
    Type
    a

Authors

Types

  • a 130
  • m 6
  • el 3
  • s 1
  • More… Less…

Themes

Subjects

Classifications