Search (15 results, page 1 of 1)

0.31449008 = sum of:
  0.00823978 = product of:
    0.03295912 = sum of:
      0.03295912 = weight(_text_:based in 3368) [ClassicSimilarity], result of:
        0.03295912 = score(doc=3368,freq=2.0), product of:
          0.14144066 = queryWeight, product of:
            3.0129938 = idf(docFreq=5906, maxDocs=44218)
            0.04694356 = queryNorm
          0.23302436 = fieldWeight in 3368, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.0129938 = idf(docFreq=5906, maxDocs=44218)
            0.0546875 = fieldNorm(doc=3368)
    0.25 = coord(1/4)
  0.15808989 = weight(_text_:term in 3368) [ClassicSimilarity], result of:
    0.15808989 = score(doc=3368,freq=8.0), product of:
      0.21904005 = queryWeight, product of:
        4.66603 = idf(docFreq=1130, maxDocs=44218)
        0.04694356 = queryNorm
      0.72173965 = fieldWeight in 3368, product of:
        2.828427 = tf(freq=8.0), with freq of:
          8.0 = termFreq=8.0
        4.66603 = idf(docFreq=1130, maxDocs=44218)
        0.0546875 = fieldNorm(doc=3368)
  0.12589967 = weight(_text_:frequency in 3368) [ClassicSimilarity], result of:
    0.12589967 = score(doc=3368,freq=2.0), product of:
      0.27643865 = queryWeight, product of:
        5.888745 = idf(docFreq=332, maxDocs=44218)
        0.04694356 = queryNorm
      0.45543438 = fieldWeight in 3368, product of:
        1.4142135 = tf(freq=2.0), with freq of:
          2.0 = termFreq=2.0
        5.888745 = idf(docFreq=332, maxDocs=44218)
        0.0546875 = fieldNorm(doc=3368)
  0.022260714 = product of:
    0.04452143 = sum of:
      0.04452143 = weight(_text_:22 in 3368) [ClassicSimilarity], result of:
        0.04452143 = score(doc=3368,freq=2.0), product of:
          0.16438834 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.04694356 = queryNorm
          0.2708308 = fieldWeight in 3368, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.0546875 = fieldNorm(doc=3368)
    0.5 = coord(1/2)

Abstract

The performance of an information retrieval or text and media filtering system may be determined through analytic methods as well as by traditional simulation or experimental methods. These analytic methods can provide precise statements about expected performance. They can thus determine which of 2 similarly performing systems is superior. For both a single query terms and for a multiple query term retrieval model, a model for comparing the performance of different probabilistic retrieval methods is developed. This method may be used in computing the average search length for a query, given only knowledge of database parameter values. Describes predictive models for inverse document frequency, binary independence, and relevance feedback based retrieval and filtering. Simulation illustrate how the single term model performs and sample performance predictions are given for single term and multiple term problems

Date

22. 2.1996 13:14:10

0.051439807 = product of:
  0.10287961 = sum of:
    0.0070626684 = product of:
      0.028250674 = sum of:
        0.028250674 = weight(_text_:based in 6976) [ClassicSimilarity], result of:
          0.028250674 = score(doc=6976,freq=2.0), product of:
            0.14144066 = queryWeight, product of:
              3.0129938 = idf(docFreq=5906, maxDocs=44218)
              0.04694356 = queryNorm
            0.19973516 = fieldWeight in 6976, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.0129938 = idf(docFreq=5906, maxDocs=44218)
              0.046875 = fieldNorm(doc=6976)
      0.25 = coord(1/4)
    0.09581695 = weight(_text_:term in 6976) [ClassicSimilarity], result of:
      0.09581695 = score(doc=6976,freq=4.0), product of:
        0.21904005 = queryWeight, product of:
          4.66603 = idf(docFreq=1130, maxDocs=44218)
          0.04694356 = queryNorm
        0.4374403 = fieldWeight in 6976, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          4.66603 = idf(docFreq=1130, maxDocs=44218)
          0.046875 = fieldNorm(doc=6976)
  0.5 = coord(2/4)

Abstract

There are regularities in the statistical information provided by natural language terms about neighboring terms. We find that when phrase rank increases, moving from common to less common phrases, the value of the expected mutual information measure (EMIM) between the terms regularly decreases. Luhn's model suggests that midrange terms are the best index terms and relevance discriminators. We suggest reasons for this principle based on the empirical relationships shown here between the rank of terms within phrases and the average mutual information between terms, which we refer to as the Inverse Representation- EMIM principle. We also suggest an Inverse EMIM term weight for indexing or retrieval applications that is consistent with Luhn's distribution. An information theoretic interpretation of Zipf's Law is provided. Using the regularity noted here, we suggest that Zipf's Law is a consequence of the statistical dependencies that exist between terms, described here using information theoretic concepts.

0.051439807 = product of:
  0.10287961 = sum of:
    0.0070626684 = product of:
      0.028250674 = sum of:
        0.028250674 = weight(_text_:based in 924) [ClassicSimilarity], result of:
          0.028250674 = score(doc=924,freq=2.0), product of:
            0.14144066 = queryWeight, product of:
              3.0129938 = idf(docFreq=5906, maxDocs=44218)
              0.04694356 = queryNorm
            0.19973516 = fieldWeight in 924, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.0129938 = idf(docFreq=5906, maxDocs=44218)
              0.046875 = fieldNorm(doc=924)
      0.25 = coord(1/4)
    0.09581695 = weight(_text_:term in 924) [ClassicSimilarity], result of:
      0.09581695 = score(doc=924,freq=4.0), product of:
        0.21904005 = queryWeight, product of:
          4.66603 = idf(docFreq=1130, maxDocs=44218)
          0.04694356 = queryNorm
        0.4374403 = fieldWeight in 924, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          4.66603 = idf(docFreq=1130, maxDocs=44218)
          0.046875 = fieldNorm(doc=924)
  0.5 = coord(2/4)

Abstract

A thesaurus and an ontology provide a set of structured terms, phrases, and metadata, often in a hierarchical arrangement, that may be used to index, search, and mine documents. We describe the decisions that should be made when including a term, deciding whether a term should be subdivided into its subclasses, or determining which of more than one set of possible subclasses should be used. Based on retrospective measurements or estimates of future performance when using thesaurus terms in document ordering, decisions are made so as to maximize performance. These decisions may be used in the automatic construction of a thesaurus. The evaluation of an existing thesaurus is described, consistent with the decision criteria developed here. These kinds of user-focused decision-theoretic techniques may be applied to other hierarchical applications, such as faceted classification systems used in information architecture or the use of hierarchical terms in "breadcrumb navigation".

0.037874974 = product of:
  0.1514999 = sum of:
    0.1514999 = weight(_text_:term in 1100) [ClassicSimilarity], result of:
      0.1514999 = score(doc=1100,freq=10.0), product of:
        0.21904005 = queryWeight, product of:
          4.66603 = idf(docFreq=1130, maxDocs=44218)
          0.04694356 = queryNorm
        0.69165385 = fieldWeight in 1100, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          4.66603 = idf(docFreq=1130, maxDocs=44218)
          0.046875 = fieldNorm(doc=1100)
  0.25 = coord(1/4)

Abstract

The assignment of indexing terms and metadata to documents, data, and other information representations is considered useful, but the utility of including a single term is seldom discussed. The author discusses a simple model of document ordering and then shows how assigning index and metadata labels improves or decreases retrieval performance. The Indexing and Metadata Advantage (IMA) factor measures how indexing or assigning a metadata term helps (or hurts) ordering performance. Performance values and the associated IMA expressions are computed, consistent with several different assumptions. The economic value associated with various term assignment decisions is developed. The IMA term advantage model itself is empirically validated with computer software that shows that the analytic results obtained agree completely with the actual performance gains and losses found when ordering all sets of 14 or fewer documents. When the formulas in the software are changed to differ from this model, the predictions of the actual performance are erroneous.

0.034227468 = product of:
  0.13690987 = sum of:
    0.13690987 = weight(_text_:term in 7709) [ClassicSimilarity], result of:
      0.13690987 = score(doc=7709,freq=6.0), product of:
        0.21904005 = queryWeight, product of:
          4.66603 = idf(docFreq=1130, maxDocs=44218)
          0.04694356 = queryNorm
        0.62504494 = fieldWeight in 7709, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          4.66603 = idf(docFreq=1130, maxDocs=44218)
          0.0546875 = fieldNorm(doc=7709)
  0.25 = coord(1/4)

Abstract

Suggests a method for comparison of the use of Boolean queries and ranking documents using document and term weights, and examines their relative merits. The performance of information retrieval may be determined either by using experimental simulation, or through the application of analytic techniques that estimate the retrieval performance, given values for query and database characteristics. Using these performance predicting techniques, sample performance figures are provided for queries using the Boolean operators and, and or, as well as for probabilistic systems assuming statistical term independence or term dependence. Examines the performance of models failing to meet statistical and other assumptions

0.033876404 = product of:
  0.13550562 = sum of:
    0.13550562 = weight(_text_:term in 7390) [ClassicSimilarity], result of:
      0.13550562 = score(doc=7390,freq=2.0), product of:
        0.21904005 = queryWeight, product of:
          4.66603 = idf(docFreq=1130, maxDocs=44218)
          0.04694356 = queryNorm
        0.618634 = fieldWeight in 7390, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.66603 = idf(docFreq=1130, maxDocs=44218)
          0.09375 = fieldNorm(doc=7390)
  0.25 = coord(1/4)

0.029337829 = product of:
  0.117351316 = sum of:
    0.117351316 = weight(_text_:term in 4162) [ClassicSimilarity], result of:
      0.117351316 = score(doc=4162,freq=6.0), product of:
        0.21904005 = queryWeight, product of:
          4.66603 = idf(docFreq=1130, maxDocs=44218)
          0.04694356 = queryNorm
        0.5357528 = fieldWeight in 4162, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          4.66603 = idf(docFreq=1130, maxDocs=44218)
          0.046875 = fieldNorm(doc=4162)
  0.25 = coord(1/4)

Abstract

An analytic method of information retrieval and filtering evaluation can quantitatively predict the expected number of documents examined in retrieving a relevant document. It also allows researchers and practioners to qualitatively understand how varying different estimates of query parameter values affects retrieval performance. The incoorporation of relevance feedback to increase our knowledge about the parameters of relevant documents and the robustness of parameter estimates is modeled. Single term and two term independence models, as well as a complete term dependence model, are developed. An economic model of retrieval performance may be used to study the effects of database size and to provide analytic answers to questions comparing retrieval from small and large databases, as well as questions about the number of terms in a query. Results are presented as a performance surface, a three dimensional graph showing the effects of two independent variables on performance.

0.0269785 = product of:
  0.107914 = sum of:
    0.107914 = weight(_text_:frequency in 8525) [ClassicSimilarity], result of:
      0.107914 = score(doc=8525,freq=2.0), product of:
        0.27643865 = queryWeight, product of:
          5.888745 = idf(docFreq=332, maxDocs=44218)
          0.04694356 = queryNorm
        0.39037234 = fieldWeight in 8525, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          5.888745 = idf(docFreq=332, maxDocs=44218)
          0.046875 = fieldNorm(doc=8525)
  0.25 = coord(1/4)

Abstract

A text window is a group of words appearing in contiguous positions in text used to exploit a variety of lexical, syntactics, and semantic relationships without having to analyze the text explicitely for their structure. This supports the previously suggested idea that natural grouping of words are best treated as a unit of size 7 to 11 words, that is, plus or minus 3 to 5 words. The text retrieval experiments varying the size of windows, both with full text and with stopwords removed, support these size ranges. The characteristcs of windows that best match terms in queries are examined in detail, revealing intersting differences between those for queries with good results and those for queries with poorer results. Queries with good results tend to contain morte content word phrase and few terms with high frequency of use in the database. Information retrieval systems may benefit from expanding thesaurus-style relationships or incorporating statistical dependencies for terms within these windows

0.0039481516 = product of:
  0.015792606 = sum of:
    0.015792606 = product of:
      0.063170426 = sum of:
        0.063170426 = weight(_text_:based in 146) [ClassicSimilarity], result of:
          0.063170426 = score(doc=146,freq=10.0), product of:
            0.14144066 = queryWeight, product of:
              3.0129938 = idf(docFreq=5906, maxDocs=44218)
              0.04694356 = queryNorm
            0.44662142 = fieldWeight in 146, product of:
              3.1622777 = tf(freq=10.0), with freq of:
                10.0 = termFreq=10.0
              3.0129938 = idf(docFreq=5906, maxDocs=44218)
              0.046875 = fieldNorm(doc=146)
      0.25 = coord(1/4)
  0.25 = coord(1/4)

Abstract

Relevance and economic feedback may be used to produce an ordering of documents that supports browsing in hypermedia and digital libraries. Document classification based on the Gray code provides paths through the entire collection, each path traversing each node in the set of documents exactly once. Examines systems organizing document based on weighted and unweighted Gray codes. Relevance feedback is used to conceptually organize the collection for an individual to browse, based on that individual's interests and information needs, as reflected by their relevance judgements and user supplied economic preferences. Applies Bayesian learning theory to estimating the characteristics of documents of interest to the user and supplying an analytic model of browsing performance, based on minimising the Expected Browsing Distance. Economic feedback may be used to change the ordering of documents to benefit the user. Using these techniques, a hypermedia or digital library may order any and all available documents, not just those examined, based on the information provided by the searcher or people with similar interests

0.0029132022 = product of:
  0.011652809 = sum of:
    0.011652809 = product of:
      0.046611235 = sum of:
        0.046611235 = weight(_text_:based in 2335) [ClassicSimilarity], result of:
          0.046611235 = score(doc=2335,freq=4.0), product of:
            0.14144066 = queryWeight, product of:
              3.0129938 = idf(docFreq=5906, maxDocs=44218)
              0.04694356 = queryNorm
            0.3295462 = fieldWeight in 2335, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.0129938 = idf(docFreq=5906, maxDocs=44218)
              0.0546875 = fieldNorm(doc=2335)
      0.25 = coord(1/4)
  0.25 = coord(1/4)

Abstract

A document classifier places documents together in a linear arrangement for browsing or high-speed access by human or computerised information retrieval systems. Requirements for document classification and browsing systems are developed from similarity measures, distance measures, and the notion of subject aboutness. A requirement that documents be arranged in decreasing order of similarity as the distance from a given document increases can often not be met. Based on these requirements, information-theoretic considerations, and the Gray code, a classification system is proposed that can classifiy documents without human intervention. A measure of classifier performance is developed, and used to evaluate experimental results comparing the distance between subject headings assigned to documents given classifications from the proposed system and the Library of Congress Classification (LCC) system

0.0023542228 = product of:
  0.009416891 = sum of:
    0.009416891 = product of:
      0.037667565 = sum of:
        0.037667565 = weight(_text_:based in 2650) [ClassicSimilarity], result of:
          0.037667565 = score(doc=2650,freq=2.0), product of:
            0.14144066 = queryWeight, product of:
              3.0129938 = idf(docFreq=5906, maxDocs=44218)
              0.04694356 = queryNorm
            0.26631355 = fieldWeight in 2650, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.0129938 = idf(docFreq=5906, maxDocs=44218)
              0.0625 = fieldNorm(doc=2650)
      0.25 = coord(1/4)
  0.25 = coord(1/4)

Abstract

The use of terms from natural and social science titles and abstracts is studied from the perspective of sublanguages and their specialized dictionaries. Explores different notions of sublanguage distinctiveness. Object methods for separating hard and soft sciences are suggested based on measures of sublanguage use, dictionary characteristics, and sublanguage distinctiveness. Abstracts were automatically classified with a high degree of accuracy by using a formula that condsiders the degree of uniqueness of terms in each sublanguage. This may prove useful for text filtering of information retrieval systems

0.0023542228 = product of:
  0.009416891 = sum of:
    0.009416891 = product of:
      0.037667565 = sum of:
        0.037667565 = weight(_text_:based in 7441) [ClassicSimilarity], result of:
          0.037667565 = score(doc=7441,freq=2.0), product of:
            0.14144066 = queryWeight, product of:
              3.0129938 = idf(docFreq=5906, maxDocs=44218)
              0.04694356 = queryNorm
            0.26631355 = fieldWeight in 7441, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.0129938 = idf(docFreq=5906, maxDocs=44218)
              0.0625 = fieldNorm(doc=7441)
      0.25 = coord(1/4)
  0.25 = coord(1/4)

Abstract

State of the art review of the mechanisms of feedback in information retrieval (IR) in terms of feedback concepts and models in cybernetics and social sciences. Critically evaluates feedback research based on the traditional IR models and comparing the different approaches to automatic relevance feedback techniques, and feedback research within the framework of interactive IR models. Calls for an extension of the concept of feedback beyond relevance feedback to interactive feedback. Cites specific examples of feedback models used within IR research and presents 6 challenges to future research

0.0017656671 = product of:
  0.0070626684 = sum of:
    0.0070626684 = product of:
      0.028250674 = sum of:
        0.028250674 = weight(_text_:based in 4068) [ClassicSimilarity], result of:
          0.028250674 = score(doc=4068,freq=2.0), product of:
            0.14144066 = queryWeight, product of:
              3.0129938 = idf(docFreq=5906, maxDocs=44218)
              0.04694356 = queryNorm
            0.19973516 = fieldWeight in 4068, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.0129938 = idf(docFreq=5906, maxDocs=44218)
              0.046875 = fieldNorm(doc=4068)
      0.25 = coord(1/4)
  0.25 = coord(1/4)

Abstract

The grammars of natural languages may be learned by using genetic algorithms that reproduce and mutate grammatical rules and parts of speech tags, improving the quality of later generations of grammatical components. Syntactic rules are randomly generated and then evolve; those rules resulting in improved parsing and occasionally improved filtering performance are allowed to further propagate. The LUST system learns the characteristics of the language or subkanguage used in document abstracts by learning from the document rankings obtained from the parsed abstracts. Unlike the application of traditional linguistic rules to retrieval and filtering applications, LUST develops grammatical structures and tags without the prior imposition of some common grammatical assumptions (e.g. part of speech assumptions), producing grammars that are empirically based and are optimized for this particular application

0.0014713892 = product of:
  0.005885557 = sum of:
    0.005885557 = product of:
      0.023542227 = sum of:
        0.023542227 = weight(_text_:based in 5148) [ClassicSimilarity], result of:
          0.023542227 = score(doc=5148,freq=2.0), product of:
            0.14144066 = queryWeight, product of:
              3.0129938 = idf(docFreq=5906, maxDocs=44218)
              0.04694356 = queryNorm
            0.16644597 = fieldWeight in 5148, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.0129938 = idf(docFreq=5906, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5148)
      0.25 = coord(1/4)
  0.25 = coord(1/4)

Abstract

Documents in digital and paper libraries may be arranged, based on their topics, in order to facilitate browsing. It may seem intuitively obvious that ordering documents by their subject should improve browsing performance; the results presented in this article suggest that ordering library materials by their Gray code values and through using links consistent with the small world model of document relationships is consistent with improving browsing performance. Below, library circulation data, including ordering with Library of Congress Classification numbers and Library of Congress Subject Headings, are used to provide information useful in generating user-centered document arrangements, as well as user-independent arrangements. Documents may be linearly arranged so they can be placed in a line by topic, such as on a library shelf, or in a list on a computer display. Crossover links, jumps between a document and another document to which it is not adjacent, can be used in library databases to allow additional paths that one might take when browsing. The improvement that is obtained with different combinations of document orderings and different crossovers is examined and applications suggested.

0.0011771114 = product of:
  0.0047084456 = sum of:
    0.0047084456 = product of:
      0.018833783 = sum of:
        0.018833783 = weight(_text_:based in 1016) [ClassicSimilarity], result of:
          0.018833783 = score(doc=1016,freq=2.0), product of:
            0.14144066 = queryWeight, product of:
              3.0129938 = idf(docFreq=5906, maxDocs=44218)
              0.04694356 = queryNorm
            0.13315678 = fieldWeight in 1016, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.0129938 = idf(docFreq=5906, maxDocs=44218)
              0.03125 = fieldNorm(doc=1016)
      0.25 = coord(1/4)
  0.25 = coord(1/4)

Abstract

Relationships between terms and features are an essential component of thesauri, ontologies, and a range of controlled vocabularies. In this article, we describe ways to identify important concepts in documents using the relationships in a thesaurus or other vocabulary structures. We introduce a methodology for the analysis and modeling of the indexing process based on a weighted random walk algorithm. The primary goal of this research is the analysis of the contribution of thesaurus structure to the indexing process. The resulting models are evaluated in the context of automatic subject indexing using four collections of documents pre-indexed with 4 different thesauri (AGROVOC [UN Food and Agriculture Organization], high-energy physics taxonomy [HEP], National Agricultural Library Thesaurus [NALT], and medical subject headings [MeSH]). We also introduce a thesaurus-centric matching algorithm intended to improve the quality of candidate concepts. In all cases, the weighted random walk improves automatic indexing performance over matching alone with an increase in average precision (AP) of 9% for HEP, 11% for MeSH, 35% for NALT, and 37% for AGROVOC. The results of the analysis support our hypothesis that subject indexing is in part a browsing process, and that using the vocabulary and its structure in a thesaurus contributes to the indexing process. The amount that the vocabulary structure contributes was found to differ among the 4 thesauri, possibly due to the vocabulary used in the corresponding thesauri and the structural relationships between the terms. Each of the thesauri and the manual indexing associated with it is characterized using the methods developed here.