Search (6 results, page 1 of 1)

0.03959743 = product of:
  0.07919486 = sum of:
    0.053759433 = weight(_text_:data in 2502) [ClassicSimilarity], result of:
      0.053759433 = score(doc=2502,freq=6.0), product of:
        0.14807065 = queryWeight, product of:
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.046827413 = queryNorm
        0.3630661 = fieldWeight in 2502, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.046875 = fieldNorm(doc=2502)
    0.025435425 = product of:
      0.05087085 = sum of:
        0.05087085 = weight(_text_:processing in 2502) [ClassicSimilarity], result of:
          0.05087085 = score(doc=2502,freq=2.0), product of:
            0.18956426 = queryWeight, product of:
              4.048147 = idf(docFreq=2097, maxDocs=44218)
              0.046827413 = queryNorm
            0.26835677 = fieldWeight in 2502, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.048147 = idf(docFreq=2097, maxDocs=44218)
              0.046875 = fieldNorm(doc=2502)
      0.5 = coord(1/2)
  0.5 = coord(2/4)

Abstract

Prior efforts have shown that under certain situations retrieval effectiveness may be improved via the use of data fusion techniques. Although these improvements have been observed from the fusion of result sets from several distinct information retrieval systems, it has often been thought that fusing different document retrieval strategies in a single information retrieval system will lead to similar improvements. In this study, we show that this is not the case. We hold constant systemic differences such as parsing, stemming, phrase processing, and relevance feedback, and fuse result sets generated from highly effective retrieval strategies in the same information retrieval system. From this, we show that data fusion of highly effective retrieval strategies alone shows little or no improvement in retrieval effectiveness. Furthermore, we present a detailed analysis of the performance of modern data fusion approaches, and demonstrate the reasons why they do not perform weIl when applied to this problem. Detailed results and analyses are included to support our conclusions.

0.013439858 = product of:
  0.053759433 = sum of:
    0.053759433 = weight(_text_:data in 4811) [ClassicSimilarity], result of:
      0.053759433 = score(doc=4811,freq=6.0), product of:
        0.14807065 = queryWeight, product of:
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.046827413 = queryNorm
        0.3630661 = fieldWeight in 4811, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.046875 = fieldNorm(doc=4811)
  0.25 = coord(1/4)

Abstract

As more online databases are integrated into digital libraries, the issue of quality control of the data becomes increasingly important, especially as it relates to the effective retrieval of information. Authority work, the need to discover and reconcile variant forms of strings in bibliographical entries, will become more critical in the future. Spelling variants, misspellings, and transliteration differences will all increase the difficulty of retrieving information. We investigate a number of approximate string matching techniques that have traditionally been used to help with this problem. We then introduce the notion of approximate word matching and show how it can be used to improve detection and categorization of variant forms. We demonstrate the utility of these approaches using data from the Astrophysics Data System and show how we can reduce the human effort involved in the creation of authority files

0.0103460075 = product of:
  0.04138403 = sum of:
    0.04138403 = weight(_text_:data in 3503) [ClassicSimilarity], result of:
      0.04138403 = score(doc=3503,freq=2.0), product of:
        0.14807065 = queryWeight, product of:
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.046827413 = queryNorm
        0.2794884 = fieldWeight in 3503, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.0625 = fieldNorm(doc=3503)
  0.25 = coord(1/4)

Source

Information retrieval: data structures and algorithms. Ed.: W.B. Frakes u. R. Baeza-Yates

0.009052756 = product of:
  0.036211025 = sum of:
    0.036211025 = weight(_text_:data in 2547) [ClassicSimilarity], result of:
      0.036211025 = score(doc=2547,freq=2.0), product of:
        0.14807065 = queryWeight, product of:
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.046827413 = queryNorm
        0.24455236 = fieldWeight in 2547, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.0546875 = fieldNorm(doc=2547)
  0.25 = coord(1/4)

Abstract

The challenge of effectively bringing specific, relevant information from the global sea of data to our fingertips, has become an increasingly difficult one. Discusses how the online information industry, founded on Boolean search systems, may be evolving to take advantage of other methods, such as 'term weighting', 'relevance ranking' and 'query by example'

0.0074939844 = product of:
  0.029975938 = sum of:
    0.029975938 = product of:
      0.059951875 = sum of:
        0.059951875 = weight(_text_:processing in 2123) [ClassicSimilarity], result of:
          0.059951875 = score(doc=2123,freq=4.0), product of:
            0.18956426 = queryWeight, product of:
              4.048147 = idf(docFreq=2097, maxDocs=44218)
              0.046827413 = queryNorm
            0.3162615 = fieldWeight in 2123, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              4.048147 = idf(docFreq=2097, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2123)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

Abstract

Typing 2 or 3 keywords into a browser has become an easy and efficient way to find information. Yet, typing even short queries becomes tedious on ever shrinking (virtual) keyboards. Meanwhile, speech processing is maturing rapidly, facilitating everyday language input. Also, wearable technology can inform users proactively by listening in on their conversations or processing their social media interactions. Given these developments, everyday language may soon become the new input of choice. We present an information retrieval (IR) algorithm specifically designed to accept everyday language. It integrates two paradigms of information retrieval, previously studied in isolation; one directed mainly at the surface structure of language, the other primarily at the underlying meaning. The integration was achieved by a Markov machine that encodes meaning by its transition graph, and surface structure by the language it generates. A rigorous evaluation of the approach showed, first, that it can compete with the quality of existing language models, second, that it is more effective the more verbose the input, and third, as a consequence, that it is promising for an imminent transition from keyword input, where the onus is on the user to formulate concise queries, to a modality where users can express more freely, more informal, and more natural their need for information in everyday language.

0.006466255 = product of:
  0.02586502 = sum of:
    0.02586502 = weight(_text_:data in 1338) [ClassicSimilarity], result of:
      0.02586502 = score(doc=1338,freq=2.0), product of:
        0.14807065 = queryWeight, product of:
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.046827413 = queryNorm
        0.17468026 = fieldWeight in 1338, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1338)
  0.25 = coord(1/4)

Abstract

A user's query is considered to be an imprecise description of their information need. Automatic query expansion is the process of reformulating the original query with the goal of improving retrieval effectiveness. Many successful query expansion techniques model syntagmatic associations that infer two terms co-occur more often than by chance in natural language. However, structural linguistics relies on both syntagmatic and paradigmatic associations to deduce the meaning of a word. Given the success of dependency-based approaches to query expansion and the reliance on word meanings in the query formulation process, we argue that modeling both syntagmatic and paradigmatic information in the query expansion process improves retrieval effectiveness. This article develops and evaluates a new query expansion technique that is based on a formal, corpus-based model of word meaning that models syntagmatic and paradigmatic associations. We demonstrate that when sufficient statistical information exists, as in the case of longer queries, including paradigmatic information alone provides significant improvements in retrieval effectiveness across a wide variety of data sets. More generally, when our new query expansion approach is applied to large-scale web retrieval it demonstrates significant improvements in retrieval effectiveness over a strong baseline system, based on a commercial search engine.