Search (24 results, page 1 of 2)

0.01830075 = product of:
  0.0366015 = sum of:
    0.020587513 = weight(_text_:for in 1369) [ClassicSimilarity], result of:
      0.020587513 = score(doc=1369,freq=10.0), product of:
        0.08876751 = queryWeight, product of:
          1.8775425 = idf(docFreq=18385, maxDocs=44218)
          0.047278564 = queryNorm
        0.2319262 = fieldWeight in 1369, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          1.8775425 = idf(docFreq=18385, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1369)
    0.016013984 = product of:
      0.032027967 = sum of:
        0.032027967 = weight(_text_:22 in 1369) [ClassicSimilarity], result of:
          0.032027967 = score(doc=1369,freq=2.0), product of:
            0.16556148 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.047278564 = queryNorm
            0.19345059 = fieldWeight in 1369, 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=1369)
      0.5 = coord(1/2)
  0.5 = coord(2/4)

Abstract

Data cubes for OLAP (On-Line Analytical Processing) often need to be constructed from data located in several distributed and autonomous information sources. Such a data integration process is challenging due to semantic, syntactic, and structural heterogeneity among the data. While XML (extensible markup language) is the de facto standard for data exchange, the three types of heterogeneity remain. Moreover, popular path-oriented XML query languages, such as XQuery, require the user to know in much detail the structure of the documents to be processed and are, thus, effectively impractical in many real-world data integration tasks. Several Lowest Common Ancestor (LCA)-based XML query evaluation strategies have recently been introduced to provide a more structure-independent way to access XML documents. We shall, however, show that this approach leads in the context of certain - not uncommon - types of XML documents to undesirable results. This article introduces a novel high-level data extraction primitive that utilizes the purpose-built Smallest Possible Context (SPC) query evaluation strategy. We demonstrate, through a system prototype for OLAP data cube construction and a sample application in informetrics, that our approach has real advantages in data integration.

Date

9. 2.2008 17:22:42

Source

Journal of the American Society for Information Science and Technology. 59(2008) no.3, S.435-449

0.007307842 = product of:
  0.029231368 = sum of:
    0.029231368 = weight(_text_:for in 1753) [ClassicSimilarity], result of:
      0.029231368 = score(doc=1753,freq=14.0), product of:
        0.08876751 = queryWeight, product of:
          1.8775425 = idf(docFreq=18385, maxDocs=44218)
          0.047278564 = queryNorm
        0.32930255 = fieldWeight in 1753, product of:
          3.7416575 = tf(freq=14.0), with freq of:
            14.0 = termFreq=14.0
          1.8775425 = idf(docFreq=18385, maxDocs=44218)
          0.046875 = fieldNorm(doc=1753)
  0.25 = coord(1/4)

Abstract

Multidimensional data analysis or On-line analytical processing (OLAP) offers a single subject-oriented source for analyzing summary data based an various dimensions. We demonstrate that the OLAP approach gives a promising starting point for advanced analysis and comparison among summary data in informetrics applications. At the moment there is no single precise, commonly accepted logical/conceptual model for multidimensional analysis. This is because the requirements of applications vary considerably. We develop a conceptual/logical multidimensional model for supporting the complex and unpredictable needs of informetrics. Summary data are considered with respect of some dimensions. By changing dimensions the user may construct other views an the same summary data. We develop a multidimensional query language whose basic idea is to support the definition of views in a way, which is natural and intuitive for lay users in the informetrics area. We show that this view-oriented query language has a great expressive power and its degree of declarativity is greater than in contemporary operation-oriented or SQL (Structured Query Language)-like OLAP query languages.

Source

Journal of the American Society for Information Science and technology. 54(2003) no.10, S.939-951

0.006510343 = product of:
  0.026041372 = sum of:
    0.026041372 = weight(_text_:for in 4395) [ClassicSimilarity], result of:
      0.026041372 = score(doc=4395,freq=16.0), product of:
        0.08876751 = queryWeight, product of:
          1.8775425 = idf(docFreq=18385, maxDocs=44218)
          0.047278564 = queryNorm
        0.29336601 = fieldWeight in 4395, product of:
          4.0 = tf(freq=16.0), with freq of:
            16.0 = termFreq=16.0
          1.8775425 = idf(docFreq=18385, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4395)
  0.25 = coord(1/4)

Abstract

Purpose - To show that stem generation compares well with lemmatization as a morphological tool for a highly inflectional language for IR purposes in a best-match retrieval system. Design/methodology/approach - Effects of three different morphological methods - lemmatization, stemming and stem production - for Finnish are compared in a probabilistic IR environment (INQUERY). Evaluation is done using a four-point relevance scale which is partitioned differently in different test settings. Findings - Results show that stem production, a lighter method than morphological lemmatization, compares well with lemmatization in a best-match IR environment. Differences in performance between stem production and lemmatization are small and they are not statistically significant in most of the tested settings. It is also shown that hitherto a rather neglected method of morphological processing for Finnish, stemming, performs reasonably well although the stemmer used - a Porter stemmer implementation - is far from optimal for a morphologically complex language like Finnish. In another series of tests, the effects of compound splitting and derivational expansion of queries are tested. Practical implications - Usefulness of morphological lemmatization and stem generation for IR purposes can be estimated with many factors. On the average P-R level they seem to behave very close to each other in a probabilistic IR system. Thus, the choice of the used method with highly inflectional languages needs to be estimated along other dimensions too. Originality/value - Results are achieved using Finnish as an example of a highly inflectional language. The results are of interest for anyone who is interested in processing of morphological variation of a highly inflected language for IR purposes.

0.0064449105 = product of:
  0.025779642 = sum of:
    0.025779642 = weight(_text_:for in 4218) [ClassicSimilarity], result of:
      0.025779642 = score(doc=4218,freq=2.0), product of:
        0.08876751 = queryWeight, product of:
          1.8775425 = idf(docFreq=18385, maxDocs=44218)
          0.047278564 = queryNorm
        0.29041752 = fieldWeight in 4218, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.8775425 = idf(docFreq=18385, maxDocs=44218)
          0.109375 = fieldNorm(doc=4218)
  0.25 = coord(1/4)

0.0051468783 = product of:
  0.020587513 = sum of:
    0.020587513 = weight(_text_:for in 4545) [ClassicSimilarity], result of:
      0.020587513 = score(doc=4545,freq=10.0), product of:
        0.08876751 = queryWeight, product of:
          1.8775425 = idf(docFreq=18385, maxDocs=44218)
          0.047278564 = queryNorm
        0.2319262 = fieldWeight in 4545, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          1.8775425 = idf(docFreq=18385, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4545)
  0.25 = coord(1/4)

Abstract

This article presents a novel user-oriented interface for generalised informetric analysis and demonstrates how informetric calculations can easily and declaratively be specified through advanced data modelling techniques. The interface is declarative and at a high level. Therefore it is easy to use, flexible and extensible. It enables end users to perform basic informetric ad hoc calculations easily and often with much less effort than in contemporary online retrieval systems. It also provides several fruitful generalisations of typical informetric measurements like impact factors. These are based on substituting traditional foci of analysis, for instance journals, by other object types, such as authors, organisations or countries. In the interface, bibliographic data are modelled as complex objects (non-first normal form relations) and terminological and citation networks involving transitive relationships are modelled as binary relations for deductive processing. The interface is flexible, because it makes it easy to switch focus between various object types for informetric calculations, e.g. from authors to institutions. Moreover, it is demonstrated that all informetric data can easily be broken down by criteria that foster advanced analysis, e.g. by years or content-bearing attributes. Such modelling allows flexible data aggregation along many dimensions. These salient features emerge from the query interface's general data restructuring and aggregation capabilities combined with transitive processing capabilities. The features are illustrated by means of sample queries and results in the article.

0.0046035075 = product of:
  0.01841403 = sum of:
    0.01841403 = weight(_text_:for in 4485) [ClassicSimilarity], result of:
      0.01841403 = score(doc=4485,freq=8.0), product of:
        0.08876751 = queryWeight, product of:
          1.8775425 = idf(docFreq=18385, maxDocs=44218)
          0.047278564 = queryNorm
        0.20744109 = fieldWeight in 4485, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          1.8775425 = idf(docFreq=18385, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4485)
  0.25 = coord(1/4)

Abstract

This article investigates how consistent different newspapers are in their choice of words when writing about the same news events. News articles on the same news events were taken from three Finnish newspapers and compared in regard to their central concepts and words representing the concepts in the news texts. Consistency figures were calculated for each set of three articles (the total number of sets was sixty). Inconsistency in words and concepts was found between news articles from different newspapers. The mean value of consistency calculated on the basis of words was 65 per cent; this however depended on the article length. For short news wires consistency was 83 per cent while for long articles it was only 47 per cent. At the concept level, consistency was considerably higher, ranging from 92 per cent to 97 per cent between short and long articles. The articles also represented three categories of topic (event, process and opinion). Statistically significant differences in consistency were found in regard to length but not in regard to the categories of topic. We argue that the expression inconsistency is a clear sign of a retrieval problem and that query expansion based on semantic relationships can significantly improve retrieval performance on free-text sources.

0.0046035075 = product of:
  0.01841403 = sum of:
    0.01841403 = weight(_text_:for in 2694) [ClassicSimilarity], result of:
      0.01841403 = score(doc=2694,freq=8.0), product of:
        0.08876751 = queryWeight, product of:
          1.8775425 = idf(docFreq=18385, maxDocs=44218)
          0.047278564 = queryNorm
        0.20744109 = fieldWeight in 2694, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          1.8775425 = idf(docFreq=18385, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2694)
  0.25 = coord(1/4)

Abstract

Research evaluation is increasingly popular and important among research funding bodies and science policy makers. Various indicators have been proposed to evaluate the standing of individual scientists, institutions, journals, or countries. A simple and popular one among the indicators is the h-index, the Hirsch index (Hirsch 2005), which is an indicator for lifetime achievement of a scholar. Several other indicators have been proposed to complement or balance the h-index. However, these indicators have no conception of aging. The AR-index (Jin et al. 2007) incorporates aging but divides the received citation counts by the raw age of the publication. Consequently, the decay of a publication is very steep and insensitive to disciplinary differences. In addition, we believe that a publication becomes outdated only when it is no longer cited, not because of its age. Finally, all indicators treat citations as equally material when one might reasonably think that a citation from a heavily cited publication should weigh more than a citation froma non-cited or little-cited publication.We propose a new indicator, the Discounted Cumulated Impact (DCI) index, which devalues old citations in a smooth way. It rewards an author for receiving new citations even if the publication is old. Further, it allows weighting of the citations by the citation weight of the citing publication. DCI can be used to calculate research performance on the basis of the h-core of a scholar or any other publication data.

Content

Erratum in: Järvelin, K., O. Persson: The DCI-index: discounted cumulated impact-based research evaluation. Erratum re. In: Journal of the American Society for Information Science and Technology. 59(2008) no.14, S.2350-2352.

Source

Journal of the American Society for Information Science and Technology. 59(2008) no.9, S.1433-1440

0.00455724 = product of:
  0.01822896 = sum of:
    0.01822896 = weight(_text_:for in 5907) [ClassicSimilarity], result of:
      0.01822896 = score(doc=5907,freq=4.0), product of:
        0.08876751 = queryWeight, product of:
          1.8775425 = idf(docFreq=18385, maxDocs=44218)
          0.047278564 = queryNorm
        0.20535621 = fieldWeight in 5907, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.8775425 = idf(docFreq=18385, maxDocs=44218)
          0.0546875 = fieldNorm(doc=5907)
  0.25 = coord(1/4)

Abstract

Search key resolution power is analyzed in the context of a request, i.e., among the set of search keys for the request. Methods of characterizing the resolution power of keys automatically are studied, and the effects search keys of varying resolution power have on retrieval effectiveness are analyzed. It is shown that it often is possible to identify the best key of a query while the discrimination between the remaining keys presents problems. It is also shown that query performance is improved by suitably using the best key in a structured query. The tests were run with InQuery in a subcollection of the TREC collection, which contained some 515,000 documents

Source

Journal of the American Society for Information Science and technology. 52(2001) no.7, S.575-583

0.003986755 = product of:
  0.01594702 = sum of:
    0.01594702 = weight(_text_:for in 2533) [ClassicSimilarity], result of:
      0.01594702 = score(doc=2533,freq=6.0), product of:
        0.08876751 = queryWeight, product of:
          1.8775425 = idf(docFreq=18385, maxDocs=44218)
          0.047278564 = queryNorm
        0.17964928 = fieldWeight in 2533, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          1.8775425 = idf(docFreq=18385, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2533)
  0.25 = coord(1/4)

Abstract

The article presents the search situation transition (SST) method for analysing Web information search (WIS) processes. The idea of the method is to analyse searching behaviour, the process, in detail and connect both the searchers' actions (captured in a log) and his/her intentions and goals, which log analysis never captures. On the other hand, ex post factor surveys, while popular in WIS research, cannot capture the actual search processes. The method is presented through three facets: its domain, its procedure, and its justification. The method's domain is presented in the form of a conceptual framework which maps five central categories that influence WIS processes; the searcher, the social/organisational environment, the work task, the search task, and the process itself. The method's procedure includes various techniques for data collection and analysis. The article presents examples from real WIS processes and shows how the method can be used to identify the interplay of the categories during the processes. It is shown that the method presents a new approach in information seeking and retrieval by focusing on the search process as a phenomenon and by explicating how different information seeking factors directly affect the search process.

0.003986755 = product of:
  0.01594702 = sum of:
    0.01594702 = weight(_text_:for in 4487) [ClassicSimilarity], result of:
      0.01594702 = score(doc=4487,freq=6.0), product of:
        0.08876751 = queryWeight, product of:
          1.8775425 = idf(docFreq=18385, maxDocs=44218)
          0.047278564 = queryNorm
        0.17964928 = fieldWeight in 4487, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          1.8775425 = idf(docFreq=18385, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4487)
  0.25 = coord(1/4)

Abstract

The increasing flood of documentary information through the Internet and other information sources challenges the developers of information retrieval systems. It is not enough that an IR system is able to make a distinction between relevant and non-relevant documents. The reduction of information overload requires that IR systems provide the capability of screening the most valuable documents out of the mass of potentially or marginally relevant documents. This paper introduces a new concept-based method to analyse the text characteristics of documents at varying relevance levels. The results of the document analysis were applied in an experiment on query expansion (QE) in a probabilistic IR system. Statistical differences in textual characteristics of highly relevant and less relevant documents were investigated by applying a facet analysis technique. In highly relevant documents a larger number of aspects of the request were discussed, searchable expressions for the aspects were distributed over a larger set of text paragraphs, and a larger set of unique expressions were used per aspect than in marginally relevant documents. A query expansion experiment verified that the findings of the text analysis can be exploited in formulating more effective queries for best match retrieval in the search for highly relevant documents. The results revealed that expanded queries with concept-based structures performed better than unexpanded queries or Ñnatural languageÒ queries. Further, it was shown that highly relevant documents benefit essentially more from the concept-based QE in ranking than marginally relevant documents.

0.003986755 = product of:
  0.01594702 = sum of:
    0.01594702 = weight(_text_:for in 5225) [ClassicSimilarity], result of:
      0.01594702 = score(doc=5225,freq=6.0), product of:
        0.08876751 = queryWeight, product of:
          1.8775425 = idf(docFreq=18385, maxDocs=44218)
          0.047278564 = queryNorm
        0.17964928 = fieldWeight in 5225, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          1.8775425 = idf(docFreq=18385, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5225)
  0.25 = coord(1/4)

Abstract

Kekalainen and Jarvelin use what they term generalized, nonbinary recall and precision measures where recall is the sum of the relevance scores of the retrieved documents divided by the sum of relevance scores of all documents in the data base, and precision is the sum of the relevance scores of the retrieved documents divided by the number of documents where the relevance scores are real numbers between zero and one. Using the In-Query system and a text data base of 53,893 newspaper articles with 30 queries selected from those for which four relevance categories to provide recall measures were available, search results were evaluated by four judges. Searches were done by average key term weight, Boolean expression, and by average term weight where the terms are grouped by a synonym operator, and for each case with and without expansion of the original terms. Use of higher standards of relevance appears to increase the superiority of the best method. Some methods do a better job of getting the highly relevant documents but do not increase retrieval of marginal ones. There is evidence that generalized precision provides more equitable results, while binary precision provides undeserved merit to some methods. Generally graded relevance measures seem to provide additional insight into IR evaluation.

Source

Journal of the American Society for Information Science and technology. 53(2002) no.13, S.1120-xxxx

0.003986755 = product of:
  0.01594702 = sum of:
    0.01594702 = weight(_text_:for in 139) [ClassicSimilarity], result of:
      0.01594702 = score(doc=139,freq=6.0), product of:
        0.08876751 = queryWeight, product of:
          1.8775425 = idf(docFreq=18385, maxDocs=44218)
          0.047278564 = queryNorm
        0.17964928 = fieldWeight in 139, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          1.8775425 = idf(docFreq=18385, maxDocs=44218)
          0.0390625 = fieldNorm(doc=139)
  0.25 = coord(1/4)

Abstract

Information retrieval systems' ability to retrieve highly relevant documents has become more and more important in the age of extremely large collections, such as the World Wide Web (WWW). The authors' aim was to find out how corpus-based cross-language information retrieval (CLIR) manages in retrieving highly relevant documents. They created a Finnish-Swedish comparable corpus from two loosely related document collections and used it as a source of knowledge for query translation. Finnish test queries were translated into Swedish and run against a Swedish test collection. Graded relevance assessments were used in evaluating the results and three relevance criterion levels-liberal, regular, and stringent-were applied. The runs were also evaluated with generalized recall and precision, which weight the retrieved documents according to their relevance level. The performance of the Comparable Corpus Translation system (COCOT) was compared to that of a dictionarybased query translation program; the two translation methods were also combined. The results indicate that corpus-based CUR performs particularly well with highly relevant documents. In average precision, COCOT even matched the monolingual baseline on the highest relevance level. The performance of the different query translation methods was further analyzed by finding out reasons for poor rankings of highly relevant documents.

Source

Journal of the American Society for Information Science and Technology. 58(2007) no.3, S.322-334

0.003986755 = product of:
  0.01594702 = sum of:
    0.01594702 = weight(_text_:for in 2836) [ClassicSimilarity], result of:
      0.01594702 = score(doc=2836,freq=6.0), product of:
        0.08876751 = queryWeight, product of:
          1.8775425 = idf(docFreq=18385, maxDocs=44218)
          0.047278564 = queryNorm
        0.17964928 = fieldWeight in 2836, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          1.8775425 = idf(docFreq=18385, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2836)
  0.25 = coord(1/4)

Abstract

Purpose - The aim of this paper is to explore the possibility of retrieving information with Kohonen self-organising maps, which are known to be effective to group objects according to their similarity or dissimilarity. Design/methodology/approach - After conventional preprocessing, such as transforming into vector space, documents from a German document collection were trained for a neural network of Kohonen self-organising map type. Such an unsupervised network forms a document map from which relevant objects can be found according to queries. Findings - Self-organising maps ordered documents to groups from which it was possible to find relevant targets. Research limitations/implications - The number of documents used was moderate due to the limited number of documents associated to test topics. The training of self-organising maps entails rather long running times, which is their practical limitation. In future, the aim will be to build larger networks by compressing document matrices, and to develop document searching in them. Practical implications - With self-organising maps the distribution of documents can be visualised and relevant documents found in document collections of limited size. Originality/value - The paper reports on an approach that can be especially used to group documents and also for information search. So far self-organising maps have rarely been studied for information retrieval. Instead, they have been applied to document grouping tasks.

0.0039062058 = product of:
  0.015624823 = sum of:
    0.015624823 = weight(_text_:for in 996) [ClassicSimilarity], result of:
      0.015624823 = score(doc=996,freq=4.0), product of:
        0.08876751 = queryWeight, product of:
          1.8775425 = idf(docFreq=18385, maxDocs=44218)
          0.047278564 = queryNorm
        0.17601961 = fieldWeight in 996, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.8775425 = idf(docFreq=18385, maxDocs=44218)
          0.046875 = fieldNorm(doc=996)
  0.25 = coord(1/4)

Abstract

In order to design information retrieval (IR) learning environments and instruction, it is important to explore learning outcomes of different pedagogical solutions. Learning outcomes have seldom been evaluated in IR instruction. The particular focus of this study is the assessment of learning outcomes in an experimental, but naturalistic, learning environment compared to more traditional instruction. The 57 participants of an introductory course on IR were selected for this study, and the analysis illustrates their learning outcomes regarding both conceptual change and development of IR skill. Concept mapping of student essays was used to analyze conceptual change and log-files of search exercises provided data for performance assessment. Students in the experimental learning environment changed their conceptions more regarding linguistic aspects of IR and paid more emphasis on planning and management of search process. Performance assessment indicates that anchored instruction and scaffolding with an instructional tool, the IR Game, with performance feedback enables students to construct queries with fewer semantic knowledge errors also in operational IR systems.

0.0039062058 = product of:
  0.015624823 = sum of:
    0.015624823 = weight(_text_:for in 1349) [ClassicSimilarity], result of:
      0.015624823 = score(doc=1349,freq=4.0), product of:
        0.08876751 = queryWeight, product of:
          1.8775425 = idf(docFreq=18385, maxDocs=44218)
          0.047278564 = queryNorm
        0.17601961 = fieldWeight in 1349, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.8775425 = idf(docFreq=18385, maxDocs=44218)
          0.046875 = fieldNorm(doc=1349)
  0.25 = coord(1/4)

Abstract

In this article, the authors present evaluation results for transitive dictionary-based cross-language information retrieval (CLIR) using graded relevance assessments in a best match retrieval environment. A text database containing newspaper articles and a related set of 35 search topics were used in the tests. Source language topics (in English, German, and Swedish) were automatically translated into the target language (Finnish) via an intermediate (or pivot) language. Effectiveness of the transitively translated queries was compared to that of the directly translated and monolingual Finnish queries. Pseudo-relevance feedback (PRF) was also used to expand the original transitive target queries. Cross-language information retrieval performance was evaluated on three relevance thresholds: stringent, regular, and liberal. The transitive translations performed well achieving, on the average, 85-93% of the direct translation performance, and 66-72% of monolingual performance. Moreover, PRF was successful in raising the performance of transitive translation routes in absolute terms as well as in relation to monolingual and direct translation performance applying PRF.

Source

Journal of the American Society for Information Science and Technology. 59(2008) no.3, S.476-488

0.0036828062 = product of:
  0.014731225 = sum of:
    0.014731225 = weight(_text_:for in 2332) [ClassicSimilarity], result of:
      0.014731225 = score(doc=2332,freq=2.0), product of:
        0.08876751 = queryWeight, product of:
          1.8775425 = idf(docFreq=18385, maxDocs=44218)
          0.047278564 = queryNorm
        0.16595288 = fieldWeight in 2332, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.8775425 = idf(docFreq=18385, maxDocs=44218)
          0.0625 = fieldNorm(doc=2332)
  0.25 = coord(1/4)

Source

Journal of the American Society for Information Science and Technology. 59(2008) no.14, S.2350-2352

0.0032551715 = product of:
  0.013020686 = sum of:
    0.013020686 = weight(_text_:for in 5601) [ClassicSimilarity], result of:
      0.013020686 = score(doc=5601,freq=4.0), product of:
        0.08876751 = queryWeight, product of:
          1.8775425 = idf(docFreq=18385, maxDocs=44218)
          0.047278564 = queryNorm
        0.14668301 = fieldWeight in 5601, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.8775425 = idf(docFreq=18385, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5601)
  0.25 = coord(1/4)

Abstract

Purpose - To present a method for creating a comparable document collection from two document collections in different languages. Design/methodology/approach - The best query keys were extracted from a Finnish source collection (articles of the newspaper Aamulehti) with the relative average term frequency formula. The keys were translated into English with a dictionary-based query translation program. The resulting lists of words were used as queries that were run against the target collection (Los Angeles Times articles) with the nearest neighbor method. The documents were aligned with unrestricted and date-restricted alignment schemes, which were also combined. Findings - The combined alignment scheme was found the best, when the relatedness of the document pairs was assessed with a five-degree relevance scale. Of the 400 document pairs, roughly 40 percent were highly or fairly related and 75 percent included at least lexical similarity. Research limitations/implications - The number of alignment pairs was small due to the short common time period of the two collections, and their geographical (and thus, topical) remoteness. In future, our aim is to build larger comparable corpora in various languages and use them as source of translation knowledge for the purposes of cross-language information retrieval (CLIR). Practical implications - Readily available parallel corpora are scarce. With this method, two unrelated document collections can relatively easily be aligned to create a CLIR resource. Originality/value - The method can be applied to weakly linked collections and morphologically complex languages, such as Finnish.

0.0032224553 = product of:
  0.012889821 = sum of:
    0.012889821 = weight(_text_:for in 3901) [ClassicSimilarity], result of:
      0.012889821 = score(doc=3901,freq=2.0), product of:
        0.08876751 = queryWeight, product of:
          1.8775425 = idf(docFreq=18385, maxDocs=44218)
          0.047278564 = queryNorm
        0.14520876 = fieldWeight in 3901, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.8775425 = idf(docFreq=18385, maxDocs=44218)
          0.0546875 = fieldNorm(doc=3901)
  0.25 = coord(1/4)

Abstract

The domain of user-oriented and cognitive information retrieval (IR) is first discussed, followed by a discussion on the dimensions and types of models one may build for the domain. The focus of the present entry is on the models of user-oriented and cognitive IR, not on their empirical applications. Several models with different emphases on user-oriented and cognitive IR are presented-ranging from overall approaches and relevance models to procedural models, cognitive models, and task-based models. The present entry does not discuss empirical findings based on the models.

0.0031894038 = product of:
  0.012757615 = sum of:
    0.012757615 = weight(_text_:for in 643) [ClassicSimilarity], result of:
      0.012757615 = score(doc=643,freq=6.0), product of:
        0.08876751 = queryWeight, product of:
          1.8775425 = idf(docFreq=18385, maxDocs=44218)
          0.047278564 = queryNorm
        0.14371942 = fieldWeight in 643, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          1.8775425 = idf(docFreq=18385, maxDocs=44218)
          0.03125 = fieldNorm(doc=643)
  0.25 = coord(1/4)

Abstract

Information Retrieval (IR) is a research area both within Computer Science and Information Science. It has by and large two communities: a Computer Science oriented experimental approach and a user-oriented Information Science approach with a Social Science background. The communities hold a critical stance towards each other (e.g., Ingwersen, 1996), the latter suspecting the realism of the former, and the former suspecting the usefulness of the latter. Within Information Science the study of information seeking (IS) also has a Social Science background. There is a lot of research in each of these particular areas of information seeking and retrieval (IS&R). However, the three communities do not really communicate with each other. Why is this, and could the relationships be otherwise? Do the communities in fact belong together? Or perhaps each community is better off forgetting about the existence of the other two? We feel that the relationships between the research areas have not been properly analyzed. One way to analyze the relationships is to examine what each research area is trying to find out: which phenomena are being explained and how. We believe that IS&R research would benefit from being analytic about its frameworks, models and theories, not just at the level of meta-theories, but also much more concretely at the level of study designs. Over the years there have been calls for more context in the study of IS&R. Work tasks as well as cultural activities/interests have been proposed as the proper context for information access. For example, Wersig (1973) conceptualized information needs from the tasks perspective. He argued that in order to learn about information needs and seeking, one needs to take into account the whole active professional role of the individuals being investigated. Byström and Järvelin (1995) analysed IS processes in the light of tasks of varying complexity. Ingwersen (1996) discussed the role of tasks and their descriptions and problematic situations from a cognitive perspective on IR. Most recently, Vakkari (2003) reviewed task-based IR and Järvelin and Ingwersen (2004) proposed the extension of IS&R research toward the task context. Therefore there is much support to the task context, but how should it be applied in IS&R?

0.0027621046 = product of:
  0.0110484185 = sum of:
    0.0110484185 = weight(_text_:for in 4922) [ClassicSimilarity], result of:
      0.0110484185 = score(doc=4922,freq=2.0), product of:
        0.08876751 = queryWeight, product of:
          1.8775425 = idf(docFreq=18385, maxDocs=44218)
          0.047278564 = queryNorm
        0.12446466 = fieldWeight in 4922, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.8775425 = idf(docFreq=18385, maxDocs=44218)
          0.046875 = fieldNorm(doc=4922)
  0.25 = coord(1/4)

Source

Journal of the American Society for Information Science and Technology. 57(2006) no.2, S.222-232