Search (1 results, page 1 of 1)

0.0072702593 = product of:
  0.036351297 = sum of:
    0.036351297 = weight(_text_:thesaurus in 2462) [ClassicSimilarity], result of:
      0.036351297 = score(doc=2462,freq=2.0), product of:
        0.23732872 = queryWeight, product of:
          4.6210785 = idf(docFreq=1182, maxDocs=44218)
          0.051357865 = queryNorm
        0.15316856 = fieldWeight in 2462, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.6210785 = idf(docFreq=1182, maxDocs=44218)
          0.0234375 = fieldNorm(doc=2462)
  0.2 = coord(1/5)

Abstract

A number of approaches have been developed in information retrieval to address the problems caused by the variability in word usage. Stemming is a popular technique used to normalize some kinds of surface-level variability by converting words to their morphological root. For example, the words "retrieve," "retrieval," "retrieved," and "retrieving" would all be converted to their root form, "retrieve." The root form is used for both document and query processing. Stemming sometimes helps retrieval, although not much (Harman, 1991; Hull, 1996). And, it does not address Gases where related words are not morphologically related (e.g., physician and doctor). Controlled vocabularies have also been used to limit variability by requiring that query and index terms belong to a pre-defined set of terms. Documents are indexed by a specified or authorized list of subject headings or index terms, called the controlled vocabulary. Library of Congress Subject Headings, Medical Subject Headings, Association for Computing Machinery (ACM) keywords, and Yellow Pages headings are examples of controlled vocabularies. If searchers can find the right controlled vocabulary terms, they do not have to think of all the morphologically related or synonymous terms that authors might have used. However, assigning controlled vocabulary terms in a consistent and thorough manner is a time-consuming and usually manual process. A good deal of research has been published about the effectiveness of controlled vocabulary indexing compared to full text indexing (e.g., Bates, 1998; Lancaster, 1986; Svenonius, 1986). The combination of both full text and controlled vocabularies is often better than either alone, although the size of the advantage is variable (Lancaster, 1986; Markey, Atherton, & Newton, 1982; Srinivasan, 1996). Richer thesauri have also been used to provide synonyms, generalizations, and specializations of users' search terms (see Srinivasan, 1992, for a review). Controlled vocabularies and thesaurus entries can be generated either manually or by the automatic analysis of large collections of texts.