Search (9 results, page 1 of 1)

0.009239726 = product of:
  0.027719175 = sum of:
    0.027719175 = weight(_text_:on in 3369) [ClassicSimilarity], result of:
      0.027719175 = score(doc=3369,freq=6.0), product of:
        0.109763056 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.04990557 = queryNorm
        0.25253648 = fieldWeight in 3369, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.046875 = fieldNorm(doc=3369)
  0.33333334 = coord(1/3)

Abstract

The ENQUIRE project forms part of a series of investigations on query expansion in the Okapi experimental text retrieval system. A configurable user interface was implemented as an evaluative tool and tested in two locations on two different databases: the library catalogue of The London Business SChool and the computing section of INSPEC. The system offered a range of possible strategies based on thesaural terms for reformulating queries. These could be initiated automatically by the system or interactively with the user. The formative phase of the evaluation established the appropriateness and usability of the interface as well as users' perceptions of the underlying functionality. The aim of the large scale field trial was to determine to what extent user would select thesaural terms suggested by the system to reformulate queries, and to evaluate the effectiveness of a new dynamic form of query expansion implemented for this project

0.008801571 = product of:
  0.026404712 = sum of:
    0.026404712 = weight(_text_:on in 430) [ClassicSimilarity], result of:
      0.026404712 = score(doc=430,freq=4.0), product of:
        0.109763056 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.04990557 = queryNorm
        0.24056101 = fieldWeight in 430, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.0546875 = fieldNorm(doc=430)
  0.33333334 = coord(1/3)

Abstract

Explores interface design raised by the development and evaluation of Okapi, a highly interactive information retrieval system based on a probabilistic retrieval model with relevance feedback. It uses terms frequency weighting functions to display retrieved items in a best match ranked order; it can also find additional items similar to those marked as relevant by the searcher. Compares the effectiveness of automatic and interactive query expansion in different user interface environments. focuses on the nature of interaction in information retrieval and the interrelationship between functional visibility, the user's cognitive loading and the balance of control between user and system

0.00622365 = product of:
  0.01867095 = sum of:
    0.01867095 = weight(_text_:on in 7734) [ClassicSimilarity], result of:
      0.01867095 = score(doc=7734,freq=2.0), product of:
        0.109763056 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.04990557 = queryNorm
        0.17010231 = fieldWeight in 7734, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.0546875 = fieldNorm(doc=7734)
  0.33333334 = coord(1/3)

Abstract

In principle, the 'knowledge' encoded in a thesaurus can be exploited in many ways to help users clarify their information needs and enhance query performance, but attempts to automate this process via AI techniques face many practical difficulties. In the short term it may be more useful to improve support for direct interactive use of thesauri. We discuss some of the issues which have arisen when building an interface for thesaurus navigation and query enhancement, drawing on logs and user feedback from ongoing small-scale experiments

0.00622365 = product of:
  0.01867095 = sum of:
    0.01867095 = weight(_text_:on in 5806) [ClassicSimilarity], result of:
      0.01867095 = score(doc=5806,freq=2.0), product of:
        0.109763056 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.04990557 = queryNorm
        0.17010231 = fieldWeight in 5806, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.0546875 = fieldNorm(doc=5806)
  0.33333334 = coord(1/3)

Abstract

Reports on work in progress to define an object oriented model of a probabilistic information retrieval system (OKAPI), the central component of which is the query itself. Considers how to represent queries both internally and at the user interface level, and their relationship with other components of the model. The model will form the basis of a configurable user interface, which allows controlled experiments to be undertaken, and could be adapted to the needs of different users accessing different databases. Implementation will involve the use of a high level interpreted scripting language for overall control, communicating with an internal model and an interface model, designed and developed using object oriented techniques

0.00622365 = product of:
  0.01867095 = sum of:
    0.01867095 = weight(_text_:on in 7446) [ClassicSimilarity], result of:
      0.01867095 = score(doc=7446,freq=2.0), product of:
        0.109763056 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.04990557 = queryNorm
        0.17010231 = fieldWeight in 7446, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.0546875 = fieldNorm(doc=7446)
  0.33333334 = coord(1/3)

Footnote

Contribution to a thematic issue on Okapi and information retrieval research

0.00622365 = product of:
  0.01867095 = sum of:
    0.01867095 = weight(_text_:on in 4705) [ClassicSimilarity], result of:
      0.01867095 = score(doc=4705,freq=2.0), product of:
        0.109763056 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.04990557 = queryNorm
        0.17010231 = fieldWeight in 4705, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.0546875 = fieldNorm(doc=4705)
  0.33333334 = coord(1/3)

Footnote

Contribution to a thematic issue on Okapi and information retrieval research

0.0053345575 = product of:
  0.016003672 = sum of:
    0.016003672 = weight(_text_:on in 4435) [ClassicSimilarity], result of:
      0.016003672 = score(doc=4435,freq=2.0), product of:
        0.109763056 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.04990557 = queryNorm
        0.14580199 = fieldWeight in 4435, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.046875 = fieldNorm(doc=4435)
  0.33333334 = coord(1/3)

Abstract

This study explored how the expression of search facets and relevance feedback (RF) by users was related to search success in interactive and automatic query expansion in the course of the search process. Search success was measured both in the number of relevant documents retrieved, whether identified by users or not. Research design consisted of 26 users searching for four TREC topics in Okapi IR system, half of the searchers using interactive and half automatic query expansion based on RF. The search logs were recorded, and the users filled in questionnaires for each topic concerning various features of searching. The results showed that the exhaustivity of the query was the most significant predictor of search success. Interactive expansion led to better search success than automatic expansion if all retrieved relevant items were counted, but there was no difference between the methods if only those items recognised relevant by users were observed. The analysis showed that the difference was facilitated by the liberal relevance criterion used in TREC not favouring highly relevant documents in evaluation.

0.0044454644 = product of:
  0.013336393 = sum of:
    0.013336393 = weight(_text_:on in 601) [ClassicSimilarity], result of:
      0.013336393 = score(doc=601,freq=2.0), product of:
        0.109763056 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.04990557 = queryNorm
        0.121501654 = fieldWeight in 601, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.0390625 = fieldNorm(doc=601)
  0.33333334 = coord(1/3)

Abstract

This article describes an evaluation of the Kea automatic keyphrase extraction algorithm. Document keyphrases are conventionally used as concise descriptors of document content, and are increasingly used in novel ways, including document clustering, searching and browsing interfaces, and retrieval engines. However, it is costly and time consuming to manually assign keyphrases to documents, motivating the development of tools that automatically perform this function. Previous studies have evaluated Kea's performance by measuring its ability to identify author keywords and keyphrases, but this methodology has a number of well-known limitations. The results presented in this article are based on evaluations by human assessors of the quality and appropriateness of Kea keyphrases. The results indicate that, in general, Kea produces keyphrases that are rated positively by human assessors. However, typical Kea settings can degrade performance, particularly those relating to keyphrase length and domain specificity. We found that for some settings, Kea's performance is better than that of similar systems, and that Kea's ranking of extracted keyphrases is effective. We also determined that author-specified keyphrases appear to exhibit an inherent ranking, and that they are rated highly and therefore suitable for use in training and evaluation of automatic keyphrasing systems.

0.0044454644 = product of:
  0.013336393 = sum of:
    0.013336393 = weight(_text_:on in 5354) [ClassicSimilarity], result of:
      0.013336393 = score(doc=5354,freq=2.0), product of:
        0.109763056 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.04990557 = queryNorm
        0.121501654 = fieldWeight in 5354, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5354)
  0.33333334 = coord(1/3)

Abstract

Human Information Behavior (HIB) research commonly examines behavior in the context of why information is acquired and how it will be used, but usually at the level of the work or everyday-life tasks the information will support. HIB has not been examined in detail at the broader contextual level of intellectual purpose (that is, the higher-order conceptual tasks the information was acquired to support). Examination at this level can enhance holistic understanding of HIB as a "means to an intellectual end" and inform the design of digital information environments that support information interaction for specific intellectual purposes. We investigate information-based ideation (IBI) as a specific intellectual information acquisition and use context by conducting Critical Incident-style interviews with 10 game designers, focusing on how they interact with information to generate and develop creative design ideas. Our findings give rise to a framework of their ideation-focused HIB, which systems designers can leverage to reason about how best to support certain behaviors to drive design ideation. These findings emphasize the importance of intellectual purpose as a driver for acquisition and desired outcome of use.