Search (140 results, page 2 of 7)

  • × theme_ss:"Automatisches Indexieren"
  • × year_i:[1990 TO 2000}
  1. Hersh, W.R.; Hickam, D.H.: ¬A comparison of two methods for indexing and retrieval from a full-text medical database (1992) 0.00
    0.0029000505 = product of:
      0.005800101 = sum of:
        0.005800101 = product of:
          0.011600202 = sum of:
            0.011600202 = weight(_text_:a in 4526) [ClassicSimilarity], result of:
              0.011600202 = score(doc=4526,freq=12.0), product of:
                0.053105544 = queryWeight, product of:
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046056706 = queryNorm
                0.21843673 = fieldWeight in 4526, product of:
                  3.4641016 = tf(freq=12.0), with freq of:
                    12.0 = termFreq=12.0
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.0546875 = fieldNorm(doc=4526)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    Reports results of a study of 2 information retrieval systems on a 2.000 document full text medical database. The first system, SAPHIRE, features concept based automatic indexing and statistical retrieval techniques, while the second system, SWORD, features traditional word based Boolean techniques, 16 medical students at Oregon Health Sciences Univ. each performed 10 searches and their results, recorded in terms of recall and precision, showed nearly equal performance for both systems. SAPHIRE was also compared with a version of SWORD modified to use automatic indexing and ranked retrieval. Using batch input of queries, the latter method performed slightly better
    Type
    a
  2. O'Kane, K.C.: Generating hierarchical document indices from common denominators in large document collections (1996) 0.00
    0.0029000505 = product of:
      0.005800101 = sum of:
        0.005800101 = product of:
          0.011600202 = sum of:
            0.011600202 = weight(_text_:a in 4037) [ClassicSimilarity], result of:
              0.011600202 = score(doc=4037,freq=12.0), product of:
                0.053105544 = queryWeight, product of:
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046056706 = queryNorm
                0.21843673 = fieldWeight in 4037, product of:
                  3.4641016 = tf(freq=12.0), with freq of:
                    12.0 = termFreq=12.0
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.0546875 = fieldNorm(doc=4037)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    Describes an effective, simple and efficient algorithm for computer generation of hierarchical indices from Document Term matrices by means of calculating common denominator vectors from the document vector set. This procedure produces an intuitive, user friendly hierarchical index of a document collection not unlike that which would be expected had a manual indexer set about to create an index or outline of a collection. The resulting index, when presented with a graphical user interface, provides the user with a natural easily comprehended view of the document collection, permits general browsing and informal search activities with an access method that requires no keyboard entry or prior knowledge of the vocabulary
    Type
    a
  3. Lassalle, E.: Text retrieval : from a monolingual system to a multilingual system (1993) 0.00
    0.0029000505 = product of:
      0.005800101 = sum of:
        0.005800101 = product of:
          0.011600202 = sum of:
            0.011600202 = weight(_text_:a in 7403) [ClassicSimilarity], result of:
              0.011600202 = score(doc=7403,freq=12.0), product of:
                0.053105544 = queryWeight, product of:
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046056706 = queryNorm
                0.21843673 = fieldWeight in 7403, product of:
                  3.4641016 = tf(freq=12.0), with freq of:
                    12.0 = termFreq=12.0
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.0546875 = fieldNorm(doc=7403)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    Describes the TELMI monolingual text retrieval system and its future extension, a multilingual system. TELMI is designed for medium sized databases containing short texts. The characteristics of the system are fine-grained natural language processing (NLP); an open domain and a large scale knowledge base; automated indexing based on conceptual representation of texts and reusability of the NLP tools. Discusses the French MINITEL service, the MGS information service and the TELMI research system covering the full text system; NLP architecture; the lexical level; the syntactic level; the semantic level and an example of the use of a generic system
    Type
    a
  4. Hlava, M.M.K.: Machine aided indexing (MAI) in a multilingual environment (1993) 0.00
    0.0029000505 = product of:
      0.005800101 = sum of:
        0.005800101 = product of:
          0.011600202 = sum of:
            0.011600202 = weight(_text_:a in 7405) [ClassicSimilarity], result of:
              0.011600202 = score(doc=7405,freq=12.0), product of:
                0.053105544 = queryWeight, product of:
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046056706 = queryNorm
                0.21843673 = fieldWeight in 7405, product of:
                  3.4641016 = tf(freq=12.0), with freq of:
                    12.0 = termFreq=12.0
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.0546875 = fieldNorm(doc=7405)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    The machine aided indexing (MAI) software devloped by Access Innovations, Inc., is a semantic based, Boolean statement, rule interpreting application with 3 modules: the MA engine which accepts input files, matches terms in the knowledge base, interprets rules, and outputs a text file with suggested indexing terms; a rule building application allowing each Boolean style rule in the knowledge base to be created or modifies; and a statistical computation module which analyzes performance of the MA software against text manually indexed by professional human indexers. The MA software can be applied across multiple languages and can be used where the text to be searched is in one language and the indexes to be output are in another
    Type
    a
  5. Salton, G.; Araya, J.: On the use of clustered file organizations in information search and retrieval (1990) 0.00
    0.0028703054 = product of:
      0.005740611 = sum of:
        0.005740611 = product of:
          0.011481222 = sum of:
            0.011481222 = weight(_text_:a in 2409) [ClassicSimilarity], result of:
              0.011481222 = score(doc=2409,freq=4.0), product of:
                0.053105544 = queryWeight, product of:
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046056706 = queryNorm
                0.2161963 = fieldWeight in 2409, product of:
                  2.0 = tf(freq=4.0), with freq of:
                    4.0 = termFreq=4.0
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.09375 = fieldNorm(doc=2409)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Source
    Library classification and its functions. Int. Conf. on ..., 20.-21.6.1989, Edmonton, Alberta. Ed.: A. Nitecki u. T. Fell
    Type
    a
  6. Humphrey, S.M.: Automatic indexing of documents from journal descriptors : a preliminary investigation (1999) 0.00
    0.0028703054 = product of:
      0.005740611 = sum of:
        0.005740611 = product of:
          0.011481222 = sum of:
            0.011481222 = weight(_text_:a in 3769) [ClassicSimilarity], result of:
              0.011481222 = score(doc=3769,freq=16.0), product of:
                0.053105544 = queryWeight, product of:
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046056706 = queryNorm
                0.2161963 = fieldWeight in 3769, product of:
                  4.0 = tf(freq=16.0), with freq of:
                    16.0 = termFreq=16.0
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046875 = fieldNorm(doc=3769)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    A new, fully automated approach for indedexing documents is presented based on associating textwords in a training set of bibliographic citations with the indexing of journals. This journal-level indexing is in the form of a consistent, timely set of journal descriptors (JDs) indexing the individual journals themselves. This indexing is maintained in journal records in a serials authority database. The advantage of this novel approach is that the training set does not depend on previous manual indexing of thousands of documents (i.e., any such indexing already in the training set is not used), but rather the relatively small intellectual effort of indexing at the journal level, usually a matter of a few thousand unique journals for which retrospective indexing to maintain consistency and currency may be feasible. If successful, JD indexing would provide topical categorization of documents outside the training set, i.e., journal articles, monographs, Web documents, reports from the grey literature, etc., and therefore be applied in searching. Because JDs are quite general, corresponding to subject domains, their most problable use would be for improving or refining search results
    Type
    a
  7. Selisskaya, M.A.: Ispol'zovanie mashinnogo obucheniya pri avtomaticheskoi klassifikatsii tekstov (1999) 0.00
    0.0028703054 = product of:
      0.005740611 = sum of:
        0.005740611 = product of:
          0.011481222 = sum of:
            0.011481222 = weight(_text_:a in 375) [ClassicSimilarity], result of:
              0.011481222 = score(doc=375,freq=4.0), product of:
                0.053105544 = queryWeight, product of:
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046056706 = queryNorm
                0.2161963 = fieldWeight in 375, product of:
                  2.0 = tf(freq=4.0), with freq of:
                    4.0 = termFreq=4.0
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.09375 = fieldNorm(doc=375)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Footnote
    Übers. des Titels: Machine learning as a tool for development of automated text indexing systems
    Type
    a
  8. Garfield, E.; Sher, I.H.: KeyWords Plus: algorithmic derivative indexing (1993) 0.00
    0.00270615 = product of:
      0.0054123 = sum of:
        0.0054123 = product of:
          0.0108246 = sum of:
            0.0108246 = weight(_text_:a in 4341) [ClassicSimilarity], result of:
              0.0108246 = score(doc=4341,freq=2.0), product of:
                0.053105544 = queryWeight, product of:
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046056706 = queryNorm
                0.20383182 = fieldWeight in 4341, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.125 = fieldNorm(doc=4341)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Type
    a
  9. Smart, G.: Using language analysis to manage information (1993) 0.00
    0.00270615 = product of:
      0.0054123 = sum of:
        0.0054123 = product of:
          0.0108246 = sum of:
            0.0108246 = weight(_text_:a in 4423) [ClassicSimilarity], result of:
              0.0108246 = score(doc=4423,freq=8.0), product of:
                0.053105544 = queryWeight, product of:
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046056706 = queryNorm
                0.20383182 = fieldWeight in 4423, product of:
                  2.828427 = tf(freq=8.0), with freq of:
                    8.0 = termFreq=8.0
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.0625 = fieldNorm(doc=4423)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    The ESPRIT project SIMPR developed software to analyse documents and generate indexes for them. Of immediate application as a document indexing and classification system, this also offers a technology for information modelling that has broader implications, supporting many new uses for information management softeware. The project was based on the assumption that information can only be managed successfully by computer systems that can view the information contained in a document through the language in which the document is written, and that systems need to be sufficiently flexible to respond to the changing requirements of document use
    Type
    a
  10. Damerau, F.J.: Generating an evaluating domain-oriented multi-word terms from texts (1993) 0.00
    0.00270615 = product of:
      0.0054123 = sum of:
        0.0054123 = product of:
          0.0108246 = sum of:
            0.0108246 = weight(_text_:a in 5814) [ClassicSimilarity], result of:
              0.0108246 = score(doc=5814,freq=8.0), product of:
                0.053105544 = queryWeight, product of:
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046056706 = queryNorm
                0.20383182 = fieldWeight in 5814, product of:
                  2.828427 = tf(freq=8.0), with freq of:
                    8.0 = termFreq=8.0
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.0625 = fieldNorm(doc=5814)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    Examines techniques for automatically generating domain vocabularies from large text collections. Focuses on the problem of generating multi-word vocabulary terms (specifically pairs). Discusses statistical issues associated with word co-occurrences likely to be of use in a natural language interface. Provides a more objective evaluation of the selection procedures. As substantial experimentation with subjects using a working query system is absent, all evaluation is necessarily subjective. Uses surrogate for experimentation by relying on pre-existing dictionaries as indicators of domain relevance
    Type
    a
  11. Garfield, E.; Sager, N.: Mechanical indexing, structural linguistics and information retrieval (1993) 0.00
    0.00270615 = product of:
      0.0054123 = sum of:
        0.0054123 = product of:
          0.0108246 = sum of:
            0.0108246 = weight(_text_:a in 5900) [ClassicSimilarity], result of:
              0.0108246 = score(doc=5900,freq=2.0), product of:
                0.053105544 = queryWeight, product of:
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046056706 = queryNorm
                0.20383182 = fieldWeight in 5900, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.125 = fieldNorm(doc=5900)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Type
    a
  12. Fournier, A.: ¬Les enjeux de l'indexation automatisée (1994) 0.00
    0.00270615 = product of:
      0.0054123 = sum of:
        0.0054123 = product of:
          0.0108246 = sum of:
            0.0108246 = weight(_text_:a in 2934) [ClassicSimilarity], result of:
              0.0108246 = score(doc=2934,freq=2.0), product of:
                0.053105544 = queryWeight, product of:
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046056706 = queryNorm
                0.20383182 = fieldWeight in 2934, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.125 = fieldNorm(doc=2934)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
  13. Pritchard-Schoch, T.: Natural language comes of age (1993) 0.00
    0.00270615 = product of:
      0.0054123 = sum of:
        0.0054123 = product of:
          0.0108246 = sum of:
            0.0108246 = weight(_text_:a in 2570) [ClassicSimilarity], result of:
              0.0108246 = score(doc=2570,freq=8.0), product of:
                0.053105544 = queryWeight, product of:
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046056706 = queryNorm
                0.20383182 = fieldWeight in 2570, product of:
                  2.828427 = tf(freq=8.0), with freq of:
                    8.0 = termFreq=8.0
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.0625 = fieldNorm(doc=2570)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    Discusses natural languages and the natural language implementations of Westlaw's full-text legal documents, Westlaw Is Natural. Natural language is not aritificial intelligence but a hybrid of linguistics, mathematics and statistics. Provides 3 classes of retrieval models. Explains how Westlaw processes an English query. Assesses WIN. Covers WIN enhancements; the natural language features of Congressional Quarterly's Washington Alert using a document for a query; the personal librarian front end search software and Dowquest from Dow Jones news/retrieval. Conmsiders whether natural language encourages fuzzy thinking and whether Boolean logic will still be needed
    Type
    a
  14. agi: Maschinelle und manuelle Indexierung optimieren (1999) 0.00
    0.00270615 = product of:
      0.0054123 = sum of:
        0.0054123 = product of:
          0.0108246 = sum of:
            0.0108246 = weight(_text_:a in 3889) [ClassicSimilarity], result of:
              0.0108246 = score(doc=3889,freq=2.0), product of:
                0.053105544 = queryWeight, product of:
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046056706 = queryNorm
                0.20383182 = fieldWeight in 3889, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.125 = fieldNorm(doc=3889)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Type
    a
  15. Buckley, C.; Allan, J.; Salton, G.: Automatic routing and retrieval using Smart : TREC-2 (1995) 0.00
    0.0026849252 = product of:
      0.0053698504 = sum of:
        0.0053698504 = product of:
          0.010739701 = sum of:
            0.010739701 = weight(_text_:a in 5699) [ClassicSimilarity], result of:
              0.010739701 = score(doc=5699,freq=14.0), product of:
                0.053105544 = queryWeight, product of:
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046056706 = queryNorm
                0.20223314 = fieldWeight in 5699, product of:
                  3.7416575 = tf(freq=14.0), with freq of:
                    14.0 = termFreq=14.0
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046875 = fieldNorm(doc=5699)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    The Smart information retrieval project emphazises completely automatic approaches to the understanding and retrieval of large quantities of text. The work in the TREC-2 environment continues, performing both routing and ad hoc experiments. The ad hoc work extends investigations into combining global similarities, giving an overall indication of how a document matches a query, with local similarities identifying a smaller part of the document that matches the query. The performance of ad hoc runs is good, but it is clear that full advantage of the available local information is not been taken advantage of. The routing experiments use conventional relevance feedback approaches to routing, but with a much greater degree of query expansion than was previously done. The length of a query vector is increased by a factor of 5 to 10 by adding terms found in previously seen relevant documents. This approach improves effectiveness by 30-40% over the original query
    Type
    a
  16. Cohen, J.D.: Highlights: language- and domain-independent automatic indexing terms for abstracting (1995) 0.00
    0.0026473717 = product of:
      0.0052947435 = sum of:
        0.0052947435 = product of:
          0.010589487 = sum of:
            0.010589487 = weight(_text_:a in 1793) [ClassicSimilarity], result of:
              0.010589487 = score(doc=1793,freq=10.0), product of:
                0.053105544 = queryWeight, product of:
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046056706 = queryNorm
                0.19940455 = fieldWeight in 1793, product of:
                  3.1622777 = tf(freq=10.0), with freq of:
                    10.0 = termFreq=10.0
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.0546875 = fieldNorm(doc=1793)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    Presents a model of drawing index terms from text. The approach uses no stop list, stemmer, or other language and domain specific component, allowing operation in any language or domain with only trivial modification. The method uses n-grams counts, achieving a function similar to, but more general than, a stemmer. The generated index terms, called 'highlights', are suitable for identifying the topic for perusal and selection. An extension is also described and demonstrated which selects index terms to represent a subset of documents, distinguishing them from the corpus. Presents some experimental results, showing operation in English, Spanish, German, Georgian, Russian and Japanese
    Type
    a
  17. Taylor, S.L.: Integrating natural language understanding with document structure analysis (1994) 0.00
    0.0026473717 = product of:
      0.0052947435 = sum of:
        0.0052947435 = product of:
          0.010589487 = sum of:
            0.010589487 = weight(_text_:a in 1794) [ClassicSimilarity], result of:
              0.010589487 = score(doc=1794,freq=10.0), product of:
                0.053105544 = queryWeight, product of:
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046056706 = queryNorm
                0.19940455 = fieldWeight in 1794, product of:
                  3.1622777 = tf(freq=10.0), with freq of:
                    10.0 = termFreq=10.0
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.0546875 = fieldNorm(doc=1794)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    Document understanding, the interpretation of a document from its image form, is a technology area which benefits greatly from the integration of natural language processing with image processing. Develops a prototype of an Intelligent Document Understanding System (IDUS) which employs several technologies: image processing, optical character recognition, document structure analysis and text understanding in a cooperative fashion. Discusses those areas of research during development of IDUS where it is found that the most benefit from the integration of natural language processing and image processing occured: document structure analysis, OCR correction, and text analysis. Discusses 2 applications which are supported by IDUS: text retrieval and automatic generation of hypertext links
    Type
    a
  18. Krutulis, J.D.; Jacob, E.K.: ¬A theoretical model for the study of emergent structure in adaptive information networks (1995) 0.00
    0.0026473717 = product of:
      0.0052947435 = sum of:
        0.0052947435 = product of:
          0.010589487 = sum of:
            0.010589487 = weight(_text_:a in 3353) [ClassicSimilarity], result of:
              0.010589487 = score(doc=3353,freq=10.0), product of:
                0.053105544 = queryWeight, product of:
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046056706 = queryNorm
                0.19940455 = fieldWeight in 3353, product of:
                  3.1622777 = tf(freq=10.0), with freq of:
                    10.0 = termFreq=10.0
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.0546875 = fieldNorm(doc=3353)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    Attempts to automate classification have focused on mimicking the intellectual processes whereby human classifiers assign entities to mutually exclusive groups that exhibit or more shared characteristics. A more viable approach might be to construct an adaptive retrieval system that produces groupings of related entities by generating dynamic categories based on document content and on the system's emergent structure as it adapts to modifications in the database and to observed patterns of access. Presents a theoretical model for adaptive information networks using relevance feedback and genetic algorithms to generate emergent structure
    Source
    Connectedness: information, systems, people, organizations. Proceedings of CAIS/ACSI 95, the proceedings of the 23rd Annual Conference of the Canadian Association for Information Science. Ed. by Hope A. Olson and Denis B. Ward
    Type
    a
  19. Wan, T.-L.; Evens, M.; Wan, Y.-W.; Pao, Y.-Y.: Experiments with automatic indexing and a relational thesaurus in a Chinese information retrieval system (1997) 0.00
    0.0026473717 = product of:
      0.0052947435 = sum of:
        0.0052947435 = product of:
          0.010589487 = sum of:
            0.010589487 = weight(_text_:a in 956) [ClassicSimilarity], result of:
              0.010589487 = score(doc=956,freq=10.0), product of:
                0.053105544 = queryWeight, product of:
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046056706 = queryNorm
                0.19940455 = fieldWeight in 956, product of:
                  3.1622777 = tf(freq=10.0), with freq of:
                    10.0 = termFreq=10.0
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.0546875 = fieldNorm(doc=956)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    This article describes a series of experiments with an interactive Chinese information retrieval system named CIRS and an interactive relational thesaurus. 2 important issues have been explored: whether thesauri enhance the retrieval effectiveness of Chinese documents, and whether automatic indexing can complete with manual indexing in a Chinese information retrieval system. Recall and precision are used to measure and evaluate the effectiveness of the system. Statistical analysis of the recall and precision measures suggest that the use of the relational thesaurus does improve the retrieval effectiveness both in the automatic indexing environment and in the manual indexing environment and that automatic indexing is at least as good as manual indexing
    Type
    a
  20. Malone, L.C.; Wildman-Pepe, J.; Driscoll, J.R.: Evaluation of an automated keywording system (1990) 0.00
    0.0024857575 = product of:
      0.004971515 = sum of:
        0.004971515 = product of:
          0.00994303 = sum of:
            0.00994303 = weight(_text_:a in 4999) [ClassicSimilarity], result of:
              0.00994303 = score(doc=4999,freq=12.0), product of:
                0.053105544 = queryWeight, product of:
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046056706 = queryNorm
                0.18723148 = fieldWeight in 4999, product of:
                  3.4641016 = tf(freq=12.0), with freq of:
                    12.0 = termFreq=12.0
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046875 = fieldNorm(doc=4999)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    An automated keywording system has been designed ro artifically behave as a human "expert" indexer. The system was designed to keyword 100 to 800 word documents representing lessons learned from military exercises and operations. A set of 74 documents can be keyworded on an IBM PS/2 model 80 in about five minutes. This paper presents a variety of ways for statistical documenting improvements in the development of an automated keywording system over time. It is not only beneficial to have some measure of system performance for a given time, but it is also useful as attemps are made to improve a system to assess if actual statistically significant improvements have been made. Furthermore, it is useful to identify the source of any existing problems so that they can be rectified. The specifics of the automated system that was evaluated are described, and the performance measures used are discussed.
    Type
    a

Languages

Types

  • a 136
  • d 1
  • el 1
  • m 1
  • s 1
  • More… Less…