Search (26 results, page 1 of 2)

0.09111415 = product of:
  0.13667122 = sum of:
    0.08786041 = weight(_text_:retrieval in 492) [ClassicSimilarity], result of:
      0.08786041 = score(doc=492,freq=4.0), product of:
        0.15490976 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.051211275 = queryNorm
        0.5671716 = fieldWeight in 492, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.09375 = fieldNorm(doc=492)
    0.04881081 = product of:
      0.09762162 = sum of:
        0.09762162 = weight(_text_:conference in 492) [ClassicSimilarity], result of:
          0.09762162 = score(doc=492,freq=2.0), product of:
            0.19418365 = queryWeight, product of:
              3.7918143 = idf(docFreq=2710, maxDocs=44218)
              0.051211275 = queryNorm
            0.50272834 = fieldWeight in 492, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.7918143 = idf(docFreq=2710, maxDocs=44218)
              0.09375 = fieldNorm(doc=492)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)

Source

Proceedings of ACM SIGIR 23rd International Conference on Research and Development in Information Retrieval. Ed. by N.J. Belkin, P. Ingwersen u. M.K. Leong

Theme

Klassifikationssysteme im Online-Retrieval

0.07592845 = product of:
  0.113892674 = sum of:
    0.073217005 = weight(_text_:retrieval in 6417) [ClassicSimilarity], result of:
      0.073217005 = score(doc=6417,freq=4.0), product of:
        0.15490976 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.051211275 = queryNorm
        0.47264296 = fieldWeight in 6417, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.078125 = fieldNorm(doc=6417)
    0.040675674 = product of:
      0.08135135 = sum of:
        0.08135135 = weight(_text_:conference in 6417) [ClassicSimilarity], result of:
          0.08135135 = score(doc=6417,freq=2.0), product of:
            0.19418365 = queryWeight, product of:
              3.7918143 = idf(docFreq=2710, maxDocs=44218)
              0.051211275 = queryNorm
            0.41894025 = fieldWeight in 6417, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.7918143 = idf(docFreq=2710, maxDocs=44218)
              0.078125 = fieldNorm(doc=6417)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)

Source

Proceedings of the 1st ACM International Conference on Digital Libraries

Theme

Semantisches Umfeld in Indexierung u. Retrieval

0.04807339 = product of:
  0.07211008 = sum of:
    0.051772244 = weight(_text_:retrieval in 5054) [ClassicSimilarity], result of:
      0.051772244 = score(doc=5054,freq=8.0), product of:
        0.15490976 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.051211275 = queryNorm
        0.33420905 = fieldWeight in 5054, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5054)
    0.020337837 = product of:
      0.040675674 = sum of:
        0.040675674 = weight(_text_:conference in 5054) [ClassicSimilarity], result of:
          0.040675674 = score(doc=5054,freq=2.0), product of:
            0.19418365 = queryWeight, product of:
              3.7918143 = idf(docFreq=2710, maxDocs=44218)
              0.051211275 = queryNorm
            0.20947012 = fieldWeight in 5054, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.7918143 = idf(docFreq=2710, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5054)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)

Abstract

As increasing numbers of non-English resources have become available on the Web, the interesting and important issue of how Web users can retrieve documents in different languages has arisen. Cross-language information retrieval (CLIP), the study of retrieving information in one language by queries expressed in another language, is a promising approach to the problem. Cross-language information retrieval has attracted much attention in recent years. Most research systems have achieved satisfactory performance on standard Text REtrieval Conference (TREC) collections such as news articles, but CLIR techniques have not been widely studied and evaluated for applications such as Web portals. In this article, the authors present their research in developing and evaluating a multilingual English-Chinese Web portal that incorporates various CLIP techniques for use in the business domain. A dictionary-based approach was adopted and combines phrasal translation, co-occurrence analysis, and pre- and posttranslation query expansion. The portal was evaluated by domain experts, using a set of queries in both English and Chinese. The experimental results showed that co-occurrence-based phrasal translation achieved a 74.6% improvement in precision over simple word-byword translation. When used together, pre- and posttranslation query expansion improved the performance slightly, achieving a 78.0% improvement over the baseline word-by-word translation approach. In general, applying CLIR techniques in Web applications shows promise.

0.030871002 = product of:
  0.092613004 = sum of:
    0.092613004 = weight(_text_:retrieval in 3845) [ClassicSimilarity], result of:
      0.092613004 = score(doc=3845,freq=10.0), product of:
        0.15490976 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.051211275 = queryNorm
        0.59785134 = fieldWeight in 3845, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0625 = fieldNorm(doc=3845)
  0.33333334 = coord(1/3)

Abstract

2 studies were conducted to investigate the cognitive processes involved in online document-based information retrieval. These studies led to the development of 5 computerised models of online document retrieval. These models were incorporated into a design of an 'intelligent' document-based retrieval system. Following a discussion of this system, discusses the broader implications of the research for the design of information retrieval sysems

0.027611865 = product of:
  0.08283559 = sum of:
    0.08283559 = weight(_text_:retrieval in 5283) [ClassicSimilarity], result of:
      0.08283559 = score(doc=5283,freq=2.0), product of:
        0.15490976 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.051211275 = queryNorm
        0.5347345 = fieldWeight in 5283, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.125 = fieldNorm(doc=5283)
  0.33333334 = coord(1/3)

0.027611865 = product of:
  0.08283559 = sum of:
    0.08283559 = weight(_text_:retrieval in 7623) [ClassicSimilarity], result of:
      0.08283559 = score(doc=7623,freq=2.0), product of:
        0.15490976 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.051211275 = queryNorm
        0.5347345 = fieldWeight in 7623, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.125 = fieldNorm(doc=7623)
  0.33333334 = coord(1/3)

0.027611865 = product of:
  0.08283559 = sum of:
    0.08283559 = weight(_text_:retrieval in 8549) [ClassicSimilarity], result of:
      0.08283559 = score(doc=8549,freq=2.0), product of:
        0.15490976 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.051211275 = queryNorm
        0.5347345 = fieldWeight in 8549, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.125 = fieldNorm(doc=8549)
  0.33333334 = coord(1/3)

0.025363116 = product of:
  0.076089345 = sum of:
    0.076089345 = weight(_text_:retrieval in 4769) [ClassicSimilarity], result of:
      0.076089345 = score(doc=4769,freq=12.0), product of:
        0.15490976 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.051211275 = queryNorm
        0.49118498 = fieldWeight in 4769, product of:
          3.4641016 = tf(freq=12.0), with freq of:
            12.0 = termFreq=12.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.046875 = fieldNorm(doc=4769)
  0.33333334 = coord(1/3)

Abstract

This paper summarizes a prototype geographical image retrieval system that demonstrates how to integrate image processing and information analysis techniques to support large-scale content-based image retrieval. By using an image as its interface, the prototype system addresses a troublesome aspect of traditional retrieval models, which require users to have complete knowledge of the low-level features of an image. In addition we describe an experiment to validate against that of human subjects in an effort to address the scarcity of research evaluating performance of an algorithm against that of human beings. The results of the experiment indicate that the system could do as well as human subjects in accomplishing the tasks of similarity analysis and image categorization. We also found that under some circumstances texture features of an image are insufficient to represent an geographic image. We believe, however, that our image retrieval system provides a promising approach to integrating image processing techniques and information retrieval algorithms

0.024160381 = product of:
  0.07248114 = sum of:
    0.07248114 = weight(_text_:retrieval in 2657) [ClassicSimilarity], result of:
      0.07248114 = score(doc=2657,freq=8.0), product of:
        0.15490976 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.051211275 = queryNorm
        0.46789268 = fieldWeight in 2657, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0546875 = fieldNorm(doc=2657)
  0.33333334 = coord(1/3)

Abstract

In the 1980s, knowledge-based techniques also made an impressive contribution to 'intelligent' information retrieval and indexing. More recently, researchers have turned to newer artificial intelligence based inductive learning techniques including neural networks, symbolic learning, and genetic algorithms grounded on diverse paradigms. These have provided great opportunities to enhance the capabilities of current information storage and retrieval systems. Provides an overview of these techniques and presents 3 popular methods: the connectionist Hopfield network; the symbolic ID3/ID5R; and evaluation based genetic algorithms in the context of information retrieval. The techniques are promising in their ability to analyze user queries, identify users' information needs, and suggest alternatives for search and can greatly complement the prevailing full text, keyword based, probabilistic, and knowledge based techniques

0.023153253 = product of:
  0.06945976 = sum of:
    0.06945976 = weight(_text_:retrieval in 1148) [ClassicSimilarity], result of:
      0.06945976 = score(doc=1148,freq=10.0), product of:
        0.15490976 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.051211275 = queryNorm
        0.44838852 = fieldWeight in 1148, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.046875 = fieldNorm(doc=1148)
  0.33333334 = coord(1/3)

Abstract

Information retrieval using probabilistic techniques has attracted significant attention on the part of researchers in information and computer science over the past few decades. In the 1980s, knowledge-based techniques also made an impressive contribution to 'intelligent' information retrieval and indexing. More recently, information science researchers have tfurned to other newer inductive learning techniques including symbolic learning, genetic algorithms, and simulated annealing. These newer techniques, which are grounded in diverse paradigms, have provided great opportunities for researchers to enhance the information processing and retrieval capabilities of current information systems. In this article, we first provide an overview of these newer techniques and their use in information retrieval research. In order to femiliarize readers with the techniques, we present 3 promising methods: the symbolic ID3 algorithm, evolution-based genetic algorithms, and simulated annealing. We discuss their knowledge representations and algorithms in the unique context of information retrieval

0.020708898 = product of:
  0.062126692 = sum of:
    0.062126692 = weight(_text_:retrieval in 501) [ClassicSimilarity], result of:
      0.062126692 = score(doc=501,freq=8.0), product of:
        0.15490976 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.051211275 = queryNorm
        0.40105087 = fieldWeight in 501, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.046875 = fieldNorm(doc=501)
  0.33333334 = coord(1/3)

Abstract

Information overload on the Web has created enormous challenges to customers selecting products for online purchases and to online businesses attempting to identify customers' preferences efficiently. Various recommender systems employing different data representations and recommendation methods are currently used to address these challenges. In this research, we developed a graph model that provides a generic data representation and can support different recommendation methods. To demonstrate its usefulness and flexibility, we developed three recommendation methods: direct retrieval, association mining, and high-degree association retrieval. We used a data set from an online bookstore as our research test-bed. Evaluation results showed that combining product content information and historical customer transaction information achieved more accurate predictions and relevant recommendations than using only collaborative information. However, comparisons among different methods showed that high-degree association retrieval did not perform significantly better than the association mining method or the direct retrieval method in our test-bed.

0.017934434 = product of:
  0.0538033 = sum of:
    0.0538033 = weight(_text_:retrieval in 1610) [ClassicSimilarity], result of:
      0.0538033 = score(doc=1610,freq=6.0), product of:
        0.15490976 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.051211275 = queryNorm
        0.34732026 = fieldWeight in 1610, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.046875 = fieldNorm(doc=1610)
  0.33333334 = coord(1/3)

Abstract

Research in information retrieval (IR) has advanced significantly in the past few decades. Many tasks, such as indexing and text categorization, can be performed automatically with minimal human effort. Machine learning has played an important role in such automation by learning various patterns such as document topics, text structures, and user interests from examples. In recent years, it has become increasingly difficult to search for useful information an the World Wide Web because of its large size and unstructured nature. Useful information and resources are often hidden in the Web. While machine learning has been successfully applied to traditional IR systems, it poses some new challenges to apply these algorithms to the Web due to its large size, link structure, diversity in content and languages, and dynamic nature. On the other hand, such characteristics of the Web also provide interesting patterns and knowledge that do not present in traditional information retrieval systems.

0.014945361 = product of:
  0.04483608 = sum of:
    0.04483608 = weight(_text_:retrieval in 6492) [ClassicSimilarity], result of:
      0.04483608 = score(doc=6492,freq=6.0), product of:
        0.15490976 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.051211275 = queryNorm
        0.28943354 = fieldWeight in 6492, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0390625 = fieldNorm(doc=6492)
  0.33333334 = coord(1/3)

Abstract

This research presents an algorithmic approach to addressing the vocabulary problem in scientific information retrieval and information sharing, using the molecular biology domain as an example. We first present a literature review of cognitive studies related to the vocabulary problem and vocabulary-based search aids (thesauri) and then discuss techniques for building robust and domain-specific thesauri to assist in cross-domain scientific information retrieval. Using a variation of the automatic thesaurus generation techniques, which we refer to as the concept space approach, we recently conducted an experiment in the molecular biology domain in which we created a C. elegans worm thesaurus of 7.657 worm-specific terms and a Drosophila fly thesaurus of 15.626 terms. About 30% of these terms overlapped, which created vocabulary paths from one subject domain to the other. Based on a cognitve study of term association involving 4 biologists, we found that a large percentage (59,6-85,6%) of the terms suggested by the subjects were identified in the cojoined fly-worm thesaurus. However, we found only a small percentage (8,4-18,1%) of the associations suggested by the subjects in the thesaurus

0.014945361 = product of:
  0.04483608 = sum of:
    0.04483608 = weight(_text_:retrieval in 1615) [ClassicSimilarity], result of:
      0.04483608 = score(doc=1615,freq=6.0), product of:
        0.15490976 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.051211275 = queryNorm
        0.28943354 = fieldWeight in 1615, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1615)
  0.33333334 = coord(1/3)

Abstract

The Medical professionals and researchers need information from reputable sources to accomplish their work. Unfortunately, the Web has a large number of documents that are irrelevant to their work, even those documents that purport to be "medically-related." This paper describes an architecture designed to integrate advanced searching and indexing algorithms, an automatic thesaurus, or "concept space," and Kohonen-based Self-Organizing Map (SOM) technologies to provide searchers with finegrained results. Initial results indicate that these systems provide complementary retrieval functionalities. HelpfulMed not only allows users to search Web pages and other online databases, but also allows them to build searches through the use of an automatic thesaurus and browse a graphical display of medical-related topics. Evaluation results for each of the different components are included. Our spidering algorithm outperformed both breadth-first search and PageRank spiders an a test collection of 100,000 Web pages. The automatically generated thesaurus performed as well as both MeSH and UMLS-systems which require human mediation for currency. Lastly, a variant of the Kohonen SOM was comparable to MeSH terms in perceived cluster precision and significantly better at perceived cluster recall.

Footnote

Teil eines Themenheftes: "Web retrieval and mining: A machine learning perspective"

Theme

Semantisches Umfeld in Indexierung u. Retrieval

0.012080191 = product of:
  0.03624057 = sum of:
    0.03624057 = weight(_text_:retrieval in 3922) [ClassicSimilarity], result of:
      0.03624057 = score(doc=3922,freq=2.0), product of:
        0.15490976 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.051211275 = queryNorm
        0.23394634 = fieldWeight in 3922, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0546875 = fieldNorm(doc=3922)
  0.33333334 = coord(1/3)

Abstract

Digital libraries of geo-spatial multimedia content are currently deficient in providing fuzzy, concept-based retrieval mechanisms to users. The main challenge is that indexing and thesaurus creation are extremely labor-intensive processes for text documents and especially for images. Recently, 800.000 declassified staellite photographs were made available by the US Geological Survey. Additionally, millions of satellite and aerial photographs are archived in national and local map libraries. Such enormous collections make human indexing and thesaurus generation methods impossible to utilize. In this article we propose a scalable method to automatically generate visual thesauri of large collections of geo-spatial media using fuzzy, unsupervised machine-learning techniques

0.010354449 = product of:
  0.031063346 = sum of:
    0.031063346 = weight(_text_:retrieval in 5202) [ClassicSimilarity], result of:
      0.031063346 = score(doc=5202,freq=2.0), product of:
        0.15490976 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.051211275 = queryNorm
        0.20052543 = fieldWeight in 5202, 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=5202)
  0.33333334 = coord(1/3)

Theme

Semantisches Umfeld in Indexierung u. Retrieval

0.010354449 = product of:
  0.031063346 = sum of:
    0.031063346 = weight(_text_:retrieval in 5704) [ClassicSimilarity], result of:
      0.031063346 = score(doc=5704,freq=2.0), product of:
        0.15490976 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.051211275 = queryNorm
        0.20052543 = fieldWeight in 5704, 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=5704)
  0.33333334 = coord(1/3)

Theme

Semantisches Umfeld in Indexierung u. Retrieval

0.010354449 = product of:
  0.031063346 = sum of:
    0.031063346 = weight(_text_:retrieval in 1247) [ClassicSimilarity], result of:
      0.031063346 = score(doc=1247,freq=2.0), product of:
        0.15490976 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.051211275 = queryNorm
        0.20052543 = fieldWeight in 1247, 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=1247)
  0.33333334 = coord(1/3)

Abstract

In this era of the Internet and distributed, multimedia computing, new and emerging classes of information systems applications have swept into the lives of office workers and people in general. From digital libraries, multimedia systems, geographic information systems, and collaborative computing to electronic commerce, virtual reality, and electronic video arts and games, these applications have created tremendous opportunities for information and computer science researchers and practitioners. As applications become more pervasive, pressing, and diverse, several well-known information retrieval (IR) problems have become even more urgent. Information overload, a result of the ease of information creation and transmission via the Internet and WWW, has become more troublesome (e.g., even stockbrokers and elementary school students, heavily exposed to various WWW search engines, are versed in such IR terminology as recall and precision). Significant variations in database formats and structures, the richness of information media (text, audio, and video), and an abundance of multilingual information content also have created severe information interoperability problems -- structural interoperability, media interoperability, and multilingual interoperability.

0.0097622685 = product of:
  0.029286804 = sum of:
    0.029286804 = weight(_text_:retrieval in 869) [ClassicSimilarity], result of:
      0.029286804 = score(doc=869,freq=4.0), product of:
        0.15490976 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.051211275 = queryNorm
        0.18905719 = fieldWeight in 869, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.03125 = fieldNorm(doc=869)
  0.33333334 = coord(1/3)

Abstract

The Internet provides an exceptional testbed for developing algorithms that can improve bowsing and searching large information spaces. Browsing and searching tasks are susceptible to problems of information overload and vocabulary differences. Much of the current research is aimed at the development and refinement of algorithms to improve browsing and searching by addressing these problems. Our research was focused on discovering whether two of the algorithms our research group has developed, a Kohonen algorithm category map for browsing, and an automatically generated concept space algorithm for searching, can help improve browsing and / or searching the Internet. Our results indicate that a Kohonen self-organizing map (SOM)-based algorithm can successfully categorize a large and eclectic Internet information space (the Entertainment subcategory of Yahoo!) into manageable sub-spaces that users can successfully navigate to locate a homepage of interest to them. The SOM algorithm worked best with browsing tasks that were very broad, and in which subjects skipped around between categories. Subjects especially liked the visual and graphical aspects of the map. Subjects who tried to do a directed search, and those that wanted to use the more familiar mental models (alphabetic or hierarchical organization) for browsing, found that the work did not work well. The results from the concept space experiment were especially encouraging. There were no significant differences among the precision measures for the set of documents identified by subject-suggested terms, thesaurus-suggested terms, and the combination of subject- and thesaurus-suggested terms. The recall measures indicated that the combination of subject- and thesaurs-suggested terms exhibited significantly better recall than subject-suggested terms alone. Furthermore, analysis of the homepages indicated that there was limited overlap between the homepages retrieved by the subject-suggested and thesaurus-suggested terms. Since the retrieval homepages for the most part were different, this suggests that a user can enhance a keyword-based search by using an automatically generated concept space. Subejcts especially liked the level of control that they could exert over the search, and the fact that the terms suggested by the thesaurus were 'real' (i.e., orininating in the homepages) and therefore guaranteed to have retrieval success

0.008628707 = product of:
  0.025886122 = sum of:
    0.025886122 = weight(_text_:retrieval in 2918) [ClassicSimilarity], result of:
      0.025886122 = score(doc=2918,freq=2.0), product of:
        0.15490976 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.051211275 = queryNorm
        0.16710453 = fieldWeight in 2918, 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=2918)
  0.33333334 = coord(1/3)

Theme

Verbale Doksprachen im Online-Retrieval