Search (8 results, page 1 of 1)

0.02277131 = product of:
  0.04554262 = sum of:
    0.028466063 = weight(_text_:information in 1428) [ClassicSimilarity], result of:
      0.028466063 = score(doc=1428,freq=22.0), product of:
        0.08850355 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.050415643 = queryNorm
        0.32163754 = fieldWeight in 1428, product of:
          4.690416 = tf(freq=22.0), with freq of:
            22.0 = termFreq=22.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1428)
    0.01707656 = product of:
      0.03415312 = sum of:
        0.03415312 = weight(_text_:22 in 1428) [ClassicSimilarity], result of:
          0.03415312 = score(doc=1428,freq=2.0), product of:
            0.17654699 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.050415643 = queryNorm
            0.19345059 = fieldWeight in 1428, 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=1428)
      0.5 = coord(1/2)
  0.5 = coord(2/4)

Abstract

Humans can make hasty, but generally robust judgements about what a text fragment is, or is not, about. Such judgements are termed information inference. This article furnishes an account of information inference from a psychologistic stance. By drawing an theories from nonclassical logic and applied cognition, an information inference mechanism is proposed that makes inferences via computations of information flow through an approximation of a conceptual space. Within a conceptual space information is represented geometrically. In this article, geometric representations of words are realized as vectors in a high dimensional semantic space, which is automatically constructed from a text corpus. Two approaches were presented for priming vector representations according to context. The first approach uses a concept combination heuristic to adjust the vector representation of a concept in the light of the representation of another concept. The second approach computes a prototypical concept an the basis of exemplar trace texts and moves it in the dimensional space according to the context. Information inference is evaluated by measuring the effectiveness of query models derived by information flow computations. Results show that information flow contributes significantly to query model effectiveness, particularly with respect to precision. Moreover, retrieval effectiveness compares favorably with two probabilistic query models, and another based an semantic association. More generally, this article can be seen as a contribution towards realizing operational systems that mimic text-based human reasoning.

Date

22. 3.2003 19:35:46

Footnote

Beitrag eines Themenheftes: Mathematical, logical, and formal methods in information retrieval

Source

Journal of the American Society for Information Science and technology. 54(2003) no.4, S.321-334

0.014607265 = product of:
  0.02921453 = sum of:
    0.01213797 = weight(_text_:information in 2755) [ClassicSimilarity], result of:
      0.01213797 = score(doc=2755,freq=4.0), product of:
        0.08850355 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.050415643 = queryNorm
        0.13714671 = fieldWeight in 2755, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2755)
    0.01707656 = product of:
      0.03415312 = sum of:
        0.03415312 = weight(_text_:22 in 2755) [ClassicSimilarity], result of:
          0.03415312 = score(doc=2755,freq=2.0), product of:
            0.17654699 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.050415643 = queryNorm
            0.19345059 = fieldWeight in 2755, 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=2755)
      0.5 = coord(1/2)
  0.5 = coord(2/4)

Abstract

Expert finding is a key task in enterprise search and has recently attracted lots of attention from both research and industry communities. Given a search topic, a prominent existing approach is to apply some information retrieval (IR) system to retrieve top ranking documents, which will then be used to derive associations between experts and the search topic based on cooccurrences. However, we argue that expert finding is more sensitive to multiple levels of associations and document features that current expert finding systems insufficiently address, including (a) multiple levels of associations between experts and search topics, (b) document internal structure, and (c) document authority. We propose a novel approach that integrates the above-mentioned three aspects as well as a query expansion technique in a two-stage model for expert finding. A systematic evaluation is conducted on TREC collections to test the performance of our approach as well as the effects of multiple windows, document features, and query expansion. These experimental results show that query expansion can dramatically improve expert finding performance with statistical significance. For three well-known IR models with or without query expansion, document internal structures help improve a single window-based approach but without statistical significance, while our novel multiple window-based approach can significantly improve the performance of a single window-based approach both with and without document internal structures.

Date

22. 3.2009 18:55:47

Source

Journal of the American Society for Information Science and Technology. 60(2009) no.4, S.694-715

0.0072827823 = product of:
  0.02913113 = sum of:
    0.02913113 = weight(_text_:information in 4077) [ClassicSimilarity], result of:
      0.02913113 = score(doc=4077,freq=4.0), product of:
        0.08850355 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.050415643 = queryNorm
        0.3291521 = fieldWeight in 4077, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.09375 = fieldNorm(doc=4077)
  0.25 = coord(1/4)

Source

SIGIR'04: Proceedings of the 27th Annual International ACM-SIGIR Conference an Research and Development in Information Retrieval. Ed.: K. Järvelin, u.a

0.0036413912 = product of:
  0.014565565 = sum of:
    0.014565565 = weight(_text_:information in 2152) [ClassicSimilarity], result of:
      0.014565565 = score(doc=2152,freq=4.0), product of:
        0.08850355 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.050415643 = queryNorm
        0.16457605 = fieldWeight in 2152, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.046875 = fieldNorm(doc=2152)
  0.25 = coord(1/4)

Abstract

In this paper, we compare a well-known semantic spacemodel, Latent Semantic Analysis (LSA) with another model, Hyperspace Analogue to Language (HAL) which is widely used in different area, especially in automatic query refinement. We conduct this comparative analysis to prove our hypothesis that with respect to ability of extracting the lexical information from a corpus of text, LSA is quite similar to HAL. We regard HAL and LSA as black boxes. Through a Pearson's correlation analysis to the outputs of these two black boxes, we conclude that LSA highly co-relates with HAL and thus there is a justification that LSA and HAL can potentially play a similar role in the area of facilitating automatic query refinement. This paper evaluates LSA in a new application area and contributes an effective way to compare different semantic space models.

Source

Computational and information science. First International Symposium, CIS 2004, Shanghai, China, December 16-18, 2004. Proceedings. Eds.: J. Zhang et al

0.0036413912 = product of:
  0.014565565 = sum of:
    0.014565565 = weight(_text_:information in 2738) [ClassicSimilarity], result of:
      0.014565565 = score(doc=2738,freq=4.0), product of:
        0.08850355 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.050415643 = queryNorm
        0.16457605 = fieldWeight in 2738, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.046875 = fieldNorm(doc=2738)
  0.25 = coord(1/4)

Abstract

This paper presents an overview of automatic methods for building domain knowledge structures (domain models) from text collections. Applications of domain models have a long history within knowledge engineering and artificial intelligence. In the last couple of decades they have surfaced noticeably as a useful tool within natural language processing, information retrieval and semantic web technology. Inspired by the ubiquitous propagation of domain model structures that are emerging in several research disciplines, we give an overview of the current research landscape and some techniques and approaches. We will also discuss trade-offs between different approaches and point to some recent trends.

Source

Information processing and management. 48(2012) no.3, S.552-568

0.0036413912 = product of:
  0.014565565 = sum of:
    0.014565565 = weight(_text_:information in 3961) [ClassicSimilarity], result of:
      0.014565565 = score(doc=3961,freq=4.0), product of:
        0.08850355 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.050415643 = queryNorm
        0.16457605 = fieldWeight in 3961, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.046875 = fieldNorm(doc=3961)
  0.25 = coord(1/4)

Abstract

Modeling multidimensional relevance in information retrieval (IR) has attracted much attention in recent years. However, most existing studies are conducted through relatively small-scale user studies, which may not reflect a real-world and natural search scenario. In this article, we propose to study the multidimensional user relevance model (MURM) on large scale query logs, which record users' various search behaviors (e.g., query reformulations, clicks and dwelling time, etc.) in natural search settings. We advance an existing MURM model (including five dimensions: topicality, novelty, reliability, understandability, and scope) by providing two additional dimensions, that is, interest and habit. The two new dimensions represent personalized relevance judgment on retrieved documents. Further, for each dimension in the enriched MURM model, a set of computable features are formulated. By conducting extensive document ranking experiments on Bing's query logs and TREC session Track data, we systematically investigated the impact of each dimension on retrieval performance and gained a series of insightful findings which may bring benefits for the design of future IR systems.

Source

Journal of the Association for Information Science and Technology. 68(2017) no.12, S.2743-2754

0.002427594 = product of:
  0.009710376 = sum of:
    0.009710376 = weight(_text_:information in 2122) [ClassicSimilarity], result of:
      0.009710376 = score(doc=2122,freq=4.0), product of:
        0.08850355 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.050415643 = queryNorm
        0.10971737 = fieldWeight in 2122, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.03125 = fieldNorm(doc=2122)
  0.25 = coord(1/4)

Series

Lecture notes in computer science : advances in information retrieval theory; 5766

Source

Second International Conference on the Theory of Information Retrieval, ICTIR 2009 Cambridge, UK, September 10-12, 2009 Proceedings. Ed.: L. Azzopardi

0.0021457102 = product of:
  0.008582841 = sum of:
    0.008582841 = weight(_text_:information in 1085) [ClassicSimilarity], result of:
      0.008582841 = score(doc=1085,freq=2.0), product of:
        0.08850355 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.050415643 = queryNorm
        0.09697737 = fieldWeight in 1085, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1085)
  0.25 = coord(1/4)

Source

Journal of the American Society for Information Science and Technology. 64(2013) no.10, S.1975-1994