Search (22 results, page 1 of 2)

0.008090839 = product of:
  0.05663587 = sum of:
    0.014268933 = weight(_text_:information in 3313) [ClassicSimilarity], result of:
      0.014268933 = score(doc=3313,freq=4.0), product of:
        0.052020688 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.029633347 = queryNorm
        0.27429342 = fieldWeight in 3313, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.078125 = fieldNorm(doc=3313)
    0.042366937 = weight(_text_:retrieval in 3313) [ClassicSimilarity], result of:
      0.042366937 = score(doc=3313,freq=4.0), product of:
        0.08963835 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.029633347 = queryNorm
        0.47264296 = fieldWeight in 3313, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.078125 = fieldNorm(doc=3313)
  0.14285715 = coord(2/14)

Abstract

Reviews the basic issues and procedures involved in natural language processing of textual material for final use in information retrieval. Covers: natural language processing; natural language understanding; syntactic and semantic analysis; parsing; knowledge bases and knowledge representation

0.006865305 = product of:
  0.04805713 = sum of:
    0.012107591 = weight(_text_:information in 3122) [ClassicSimilarity], result of:
      0.012107591 = score(doc=3122,freq=2.0), product of:
        0.052020688 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.029633347 = queryNorm
        0.23274569 = fieldWeight in 3122, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.09375 = fieldNorm(doc=3122)
    0.03594954 = weight(_text_:retrieval in 3122) [ClassicSimilarity], result of:
      0.03594954 = score(doc=3122,freq=2.0), product of:
        0.08963835 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.029633347 = queryNorm
        0.40105087 = fieldWeight in 3122, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.09375 = fieldNorm(doc=3122)
  0.14285715 = coord(2/14)

Footnote

Wiederabgedruckt in: Readings in information retrieval. Ed.: K. Sparck Jones u. P. Willett. San Francisco: Morgan Kaufmann 1997. S.313-316.

0.0056823203 = product of:
  0.03977624 = sum of:
    0.008071727 = weight(_text_:information in 3650) [ClassicSimilarity], result of:
      0.008071727 = score(doc=3650,freq=8.0), product of:
        0.052020688 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.029633347 = queryNorm
        0.1551638 = fieldWeight in 3650, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.03125 = fieldNorm(doc=3650)
    0.03170451 = weight(_text_:retrieval in 3650) [ClassicSimilarity], result of:
      0.03170451 = score(doc=3650,freq=14.0), product of:
        0.08963835 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.029633347 = queryNorm
        0.3536936 = fieldWeight in 3650, product of:
          3.7416575 = tf(freq=14.0), with freq of:
            14.0 = termFreq=14.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.03125 = fieldNorm(doc=3650)
  0.14285715 = coord(2/14)

Abstract

The attempt to computerize a process, such as indexing, abstracting, classifying, or retrieving information, begins with an analysis of the process into its intellectual and nonintellectual components. That part of the process which is amenable to computerization is mechanical or algorithmic. What is not is intellectual or creative and requires human intervention. Gerard Salton has been an innovator, experimenter, and promoter in the area of mechanized information systems since the early 1960s. He has been particularly ingenious at analyzing the process of information retrieval into its algorithmic components. He received a doctorate in applied mathematics from Harvard University before moving to the computer science department at Cornell, where he developed a prototype automatic retrieval system called SMART. Working with this system he and his students contributed for over a decade to our theoretical understanding of the retrieval process. On a more practical level, they have contributed design criteria for operating retrieval systems. The following selection presents one of the early descriptions of the SMART system; it is valuable as it shows the direction automatic retrieval methods were to take beyond simple word-matching techniques. These include various word normalization techniques to improve recall, for instance, the separation of words into stems and affixes; the correlation and clustering, using statistical association measures, of related terms; and the identification, using a concept thesaurus, of synonymous, broader, narrower, and sibling terms. They include, as weIl, techniques, both linguistic and statistical, to deal with the thorny problem of how to automatically extract from texts index terms that consist of more than one word. They include weighting techniques and various documentrequest matching algorithms. Significant among the latter are those which produce a retrieval output of citations ranked in relevante order. During the 1970s, Salton and his students went an to further refine these various techniques, particularly the weighting and statistical association measures. Many of their early innovations seem commonplace today. Some of their later techniques are still ahead of their time and await technological developments for implementation. The particular focus of the selection that follows is an the evaluation of a particular component of the SMART system, a multilingual thesaurus. By mapping English language expressions and their German equivalents to a common concept number, the thesaurus permitted the automatic processing of German language documents against English language queries and vice versa. The results of the evaluation, as it turned out, were somewhat inconclusive. However, this SMART experiment suggested in a bold and optimistic way how one might proceed to answer such complex questions as What is meant by retrieval language compatability? How it is to be achieved, and how evaluated?

Footnote

Original in: Journal of the American Society for Information Science 21(1970) no.3, S.187-194.

0.0055279788 = product of:
  0.03869585 = sum of:
    0.008737902 = weight(_text_:information in 1845) [ClassicSimilarity], result of:
      0.008737902 = score(doc=1845,freq=6.0), product of:
        0.052020688 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.029633347 = queryNorm
        0.16796975 = fieldWeight in 1845, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1845)
    0.029957948 = weight(_text_:retrieval in 1845) [ClassicSimilarity], result of:
      0.029957948 = score(doc=1845,freq=8.0), product of:
        0.08963835 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.029633347 = queryNorm
        0.33420905 = fieldWeight in 1845, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1845)
  0.14285715 = coord(2/14)

Abstract

It may be possible to improve the quality of automatic indexing systems by using complex descriptors, for example, phrases, in addition to the simple descriptors (words or word stems) that are normally used in automatically constructed representations of document content. This study is directed toward the goal of developing effective methods of identifying phrases in natural language text from which good quality phrase descriptors can be constructed. The effectiveness of one method, a simple nonsyntactic phrase indexing procedure, has been tested on five experimental document collections. The results have been analyzed in order to identify the inadequacies of the procedure, and to determine what kinds of information about text structure are needed in order to construct phrase descriptors that are good indicators of document content. Two primary conclusions have been reached: (1) In the retrieval experiments, the nonsyntactic phrase construction procedure did not consistently yield substantial improvements in effectiveness. It is therefore not likely that phrase indexing of this kind will prove to be an important method of enhancing the performance of automatic document indexing and retrieval systems in operational environments. (2) Many of the shortcomings of the nonsyntactic approach can be overcome by incorporating syntactic information into the phrase construction process. However, a general syntactic analysis facility may be required, since many useful sources of phrases cannot be exploited if only a limited inventory of syntactic patterns can be recognized. Further research should be conducted into methods of incorporating automatic syntactic analysis into content analysis for document retrieval.

Source

Journal of the American Society for Information Science. 40(1989) no.2, S.115-132

0.005054501 = product of:
  0.035381503 = sum of:
    0.011415146 = weight(_text_:information in 3632) [ClassicSimilarity], result of:
      0.011415146 = score(doc=3632,freq=4.0), product of:
        0.052020688 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.029633347 = queryNorm
        0.21943474 = fieldWeight in 3632, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0625 = fieldNorm(doc=3632)
    0.023966359 = weight(_text_:retrieval in 3632) [ClassicSimilarity], result of:
      0.023966359 = score(doc=3632,freq=2.0), product of:
        0.08963835 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.029633347 = queryNorm
        0.26736724 = fieldWeight in 3632, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0625 = fieldNorm(doc=3632)
  0.14285715 = coord(2/14)

Source

Journal of information science. 18(1992) no.5, S.343-354

0.005000397 = product of:
  0.03500278 = sum of:
    0.0050448296 = weight(_text_:information in 896) [ClassicSimilarity], result of:
      0.0050448296 = score(doc=896,freq=2.0), product of:
        0.052020688 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.029633347 = queryNorm
        0.09697737 = fieldWeight in 896, 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=896)
    0.029957948 = weight(_text_:retrieval in 896) [ClassicSimilarity], result of:
      0.029957948 = score(doc=896,freq=8.0), product of:
        0.08963835 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.029633347 = queryNorm
        0.33420905 = fieldWeight in 896, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0390625 = fieldNorm(doc=896)
  0.14285715 = coord(2/14)

Abstract

This paper deals with Swedish full text retrieval and the problem of morphological variation of query terms in the document database. The effects of combination of indexing strategies with query terms on retrieval effectiveness were studied. Three of five tested combinations involved indexing strategies that used conflation, in the form of normalization. Further, two of these three combinations used indexing strategies that employed compound splitting. Normalization and compound splitting were performed by SWETWOL, a morphological analyzer for the Swedish language. A fourth combination attempted to group related terms by right hand truncation of query terms. The four combinations were compared to each other and to a baseline combination, where no attempt was made to counteract the problem of morphological variation of query terms in the document database. The five combinations were evaluated under six different user scenarios, where each scenario simulated a certain user type. The four alternative combinations outperformed the baseline, for each user scenario. The truncation combination had the best performance under each user scenario. The main conclusion of the paper is that normalization and right hand truncation (performed by a search expert) enhanced retrieval effectiveness in comparison to the baseline. The performance of the three combinations of indexing strategies with query terms based on normalization was not far below the performance of the truncation combination.

Source

Information processing and management. 43(2007) no.1, S.81-102

0.0045768693 = product of:
  0.032038085 = sum of:
    0.008071727 = weight(_text_:information in 3502) [ClassicSimilarity], result of:
      0.008071727 = score(doc=3502,freq=2.0), product of:
        0.052020688 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.029633347 = queryNorm
        0.1551638 = fieldWeight in 3502, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0625 = fieldNorm(doc=3502)
    0.023966359 = weight(_text_:retrieval in 3502) [ClassicSimilarity], result of:
      0.023966359 = score(doc=3502,freq=2.0), product of:
        0.08963835 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.029633347 = queryNorm
        0.26736724 = fieldWeight in 3502, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0625 = fieldNorm(doc=3502)
  0.14285715 = coord(2/14)

Source

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

0.00406575 = product of:
  0.028460251 = sum of:
    0.0104854815 = weight(_text_:information in 8524) [ClassicSimilarity], result of:
      0.0104854815 = score(doc=8524,freq=6.0), product of:
        0.052020688 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.029633347 = queryNorm
        0.20156369 = fieldWeight in 8524, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.046875 = fieldNorm(doc=8524)
    0.01797477 = weight(_text_:retrieval in 8524) [ClassicSimilarity], result of:
      0.01797477 = score(doc=8524,freq=2.0), product of:
        0.08963835 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.029633347 = queryNorm
        0.20052543 = fieldWeight in 8524, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.046875 = fieldNorm(doc=8524)
  0.14285715 = coord(2/14)

Abstract

Examines how techniques in the field of natural language processing can be applied to the analysis of text in information retrieval. State of the art text searching programs cannot distinguish, for example, between occurrences of the sickness, AIDS and aids as tool or between library school and school nor equate such terms as online or on-line which are variants of the same form. To make these distinction, systems must incorporate knowledge about the meaning of words in context. Research in natural language processing has concentrated on the automatic 'understanding' of language; how to analyze the grammatical structure and meaning of text. Although many asoects of this research remain experimental, describes how these techniques to recognize spelling variants, names, acronyms, and abbreviations

Imprint

Medford, NJ : Learned Information

0.0040628472 = product of:
  0.02843993 = sum of:
    0.0049941265 = weight(_text_:information in 3645) [ClassicSimilarity], result of:
      0.0049941265 = score(doc=3645,freq=4.0), product of:
        0.052020688 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.029633347 = queryNorm
        0.0960027 = fieldWeight in 3645, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.02734375 = fieldNorm(doc=3645)
    0.023445804 = weight(_text_:retrieval in 3645) [ClassicSimilarity], result of:
      0.023445804 = score(doc=3645,freq=10.0), product of:
        0.08963835 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.029633347 = queryNorm
        0.26155996 = fieldWeight in 3645, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.02734375 = fieldNorm(doc=3645)
  0.14285715 = coord(2/14)

Abstract

The selection that follows was chosen as it represents "a very early paper an the possibilities allowed by computers an documentation." In the early 1960s computers were being used to provide simple automatic indexing systems wherein keywords were extracted from documents. The problem with such systems was that they lacked vocabulary control, thus documents related in subject matter were not always collocated in retrieval. To improve retrieval by improving recall is the raison d'être of vocabulary control tools such as classifications and thesauri. The question arose whether it was possible by automatic means to construct classes of terms, which when substituted, one for another, could be used to improve retrieval performance? One of the first theoretical approaches to this question was initiated by R. M. Needham and Karen Sparck Jones at the Cambridge Language Research Institute in England.t The question was later pursued using experimental methodologies by Sparck Jones, who, as a Senior Research Associate in the Computer Laboratory at the University of Cambridge, has devoted her life's work to research in information retrieval and automatic naturai language processing. Based an the principles of numerical taxonomy, automatic classification techniques start from the premise that two objects are similar to the degree that they share attributes in common. When these two objects are keywords, their similarity is measured in terms of the number of documents they index in common. Step 1 in automatic classification is to compute mathematically the degree to which two terms are similar. Step 2 is to group together those terms that are "most similar" to each other, forming equivalence classes of intersubstitutable terms. The technique for forming such classes varies and is the factor that characteristically distinguishes different approaches to automatic classification. The technique used by Needham and Sparck Jones, that of clumping, is described in the selection that follows. Questions that must be asked are whether the use of automatically generated classes really does improve retrieval performance and whether there is a true eco nomic advantage in substituting mechanical for manual labor. Several years after her work with clumping, Sparck Jones was to observe that while it was not wholly satisfactory in itself, it was valuable in that it stimulated research into automatic classification. To this it might be added that it was valuable in that it introduced to libraryl information science the methods of numerical taxonomy, thus stimulating us to think again about the fundamental nature and purpose of classification. In this connection it might be useful to review how automatically derived classes differ from those of manually constructed classifications: 1) the manner of their derivation is purely a posteriori, the ultimate operationalization of the principle of literary warrant; 2) the relationship between members forming such classes is essentially statistical; the members of a given class are similar to each other not because they possess the class-defining characteristic but by virtue of sharing a family resemblance; and finally, 3) automatically derived classes are not related meaningfully one to another, that is, they are not ordered in traditional hierarchical and precedence relationships.

0.003790876 = product of:
  0.02653613 = sum of:
    0.00856136 = weight(_text_:information in 2910) [ClassicSimilarity], result of:
      0.00856136 = score(doc=2910,freq=4.0), product of:
        0.052020688 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.029633347 = queryNorm
        0.16457605 = fieldWeight in 2910, 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=2910)
    0.01797477 = weight(_text_:retrieval in 2910) [ClassicSimilarity], result of:
      0.01797477 = score(doc=2910,freq=2.0), product of:
        0.08963835 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.029633347 = queryNorm
        0.20052543 = fieldWeight in 2910, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.046875 = fieldNorm(doc=2910)
  0.14285715 = coord(2/14)

Abstract

Due to natural language morphology, words can take on various morphological forms. Morphological normalisation - often used in information retrieval and text mining systems - conflates morphological variants of a word to a single representative form. In this paper, we describe an approach to lexicon-based inflectional normalisation. This approach is in between stemming and lemmatisation, and is suitable for morphological normalisation of inflectionally complex languages. To eliminate the immense effort required to compile the lexicon by hand, we focus on the problem of acquiring automatically an inflectional morphological lexicon from raw corpora. We propose a convenient and highly expressive morphology representation formalism on which the acquisition procedure is based. Our approach is applied to the morphologically complex Croatian language, but it should be equally applicable to other languages of similar morphological complexity. Experimental results show that our approach can be used to acquire a lexicon whose linguistic quality allows for rather good normalisation performance.

Source

Information processing and management. 44(2008) no.5, S.1720-1731

0.0035267244 = product of:
  0.02468707 = sum of:
    0.013980643 = weight(_text_:information in 6752) [ClassicSimilarity], result of:
      0.013980643 = score(doc=6752,freq=6.0), product of:
        0.052020688 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.029633347 = queryNorm
        0.2687516 = fieldWeight in 6752, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0625 = fieldNorm(doc=6752)
    0.010706427 = product of:
      0.032119278 = sum of:
        0.032119278 = weight(_text_:22 in 6752) [ClassicSimilarity], result of:
          0.032119278 = score(doc=6752,freq=2.0), product of:
            0.103770934 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.029633347 = queryNorm
            0.30952093 = fieldWeight in 6752, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0625 = fieldNorm(doc=6752)
      0.33333334 = coord(1/3)
  0.14285715 = coord(2/14)

Abstract

AutoSlog is a system that addresses the knowledge engineering bottleneck for information extraction. AutoSlog automatically creates domain specific dictionaries for information extraction, given an appropriate training corpus. Describes experiments with AutoSlog in terrorism, joint ventures and microelectronics domains. Compares the performance of AutoSlog across the 3 domains, discusses the lessons learned and presents results from 2 experiments which demonstrate that novice users can generate effective dictionaries using AutoSlog

Date

6. 3.1997 16:22:15

0.0028605436 = product of:
  0.020023804 = sum of:
    0.0050448296 = weight(_text_:information in 1842) [ClassicSimilarity], result of:
      0.0050448296 = score(doc=1842,freq=2.0), product of:
        0.052020688 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.029633347 = queryNorm
        0.09697737 = fieldWeight in 1842, 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=1842)
    0.014978974 = weight(_text_:retrieval in 1842) [ClassicSimilarity], result of:
      0.014978974 = score(doc=1842,freq=2.0), product of:
        0.08963835 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.029633347 = queryNorm
        0.16710453 = fieldWeight in 1842, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1842)
  0.14285715 = coord(2/14)

Abstract

Many terminology engineering processes involve the task of automatic terminology extraction: before the terminology of a given domain can be modelled, organised or standardised, important concepts (or terms) of this domain have to be identified and fed into terminological databases. These serve in further steps as a starting point for compiling dictionaries, thesauri or maybe even terminological ontologies for the domain. For the extraction of the initial concepts, extraction methods are needed that operate on specialised language texts. On the other hand, many machine learning or information retrieval applications require automatic indexing techniques. In Machine Learning applications concerned with the automatic clustering or classification of texts, often feature vectors are needed that describe the contents of a given text briefly but meaningfully. These feature vectors typically consist of a fairly small set of index terms together with weights indicating their importance. Short but meaningful descriptions of document contents as provided by good index terms are also useful to humans: some knowledge management applications (e.g. topic maps) use them as a set of basic concepts (topics). The author believes that the tasks of terminology extraction and automatic indexing have much in common and can thus benefit from the same set of basic algorithms. It is the goal of this paper to outline some methods that may be used in both contexts, but also to find the discriminating factors between the two tasks that call for the variation of parameters or application of different techniques. The discussion of these methods will be based on statistical, syntactical and especially morphological properties of (index) terms. The paper is concluded by the presentation of some qualitative and quantitative results comparing statistical and morphological methods.

0.0027546415 = product of:
  0.03856498 = sum of:
    0.03856498 = weight(_text_:wide in 5480) [ClassicSimilarity], result of:
      0.03856498 = score(doc=5480,freq=2.0), product of:
        0.1312982 = queryWeight, product of:
          4.4307585 = idf(docFreq=1430, maxDocs=44218)
          0.029633347 = queryNorm
        0.29372054 = fieldWeight in 5480, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.4307585 = idf(docFreq=1430, maxDocs=44218)
          0.046875 = fieldNorm(doc=5480)
  0.071428575 = coord(1/14)

Abstract

(Automatic) document classification is generally defined as content-based assignment of one or more predefined categories to documents. Usually, machine learning, statistical pattern recognition, or neural network approaches are used to construct classifiers automatically. In this paper we thoroughly evaluate a wide variety of these methods on a document classification task for German text. We evaluate different feature construction and selection methods and various classifiers. Our main results are: (1) feature selection is necessary not only to reduce learning and classification time, but also to avoid overfitting (even for Support Vector Machines); (2) surprisingly, our morphological analysis does not improve classification quality compared to a letter 5-gram approach; (3) Support Vector Machines are significantly better than all other classification methods

0.002420968 = product of:
  0.033893548 = sum of:
    0.033893548 = weight(_text_:retrieval in 2570) [ClassicSimilarity], result of:
      0.033893548 = score(doc=2570,freq=4.0), product of:
        0.08963835 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.029633347 = queryNorm
        0.37811437 = fieldWeight in 2570, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0625 = fieldNorm(doc=2570)
  0.071428575 = coord(1/14)

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

0.0022238013 = product of:
  0.031133216 = sum of:
    0.031133216 = weight(_text_:retrieval in 5599) [ClassicSimilarity], result of:
      0.031133216 = score(doc=5599,freq=6.0), product of:
        0.08963835 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.029633347 = queryNorm
        0.34732026 = fieldWeight in 5599, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.046875 = fieldNorm(doc=5599)
  0.071428575 = coord(1/14)

Abstract

Purpose - To evaluate the accuracy of conflation methods based on finite-state transducers (FSTs). Design/methodology/approach - Incorrectly lemmatized and stemmed forms may lead to the retrieval of inappropriate documents. Experimental studies to date have focused on retrieval performance, but very few on conflation performance. The process of normalization we used involved a linguistic toolbox that allowed us to construct, through graphic interfaces, electronic dictionaries represented internally by FSTs. The lexical resources developed were applied to a Spanish test corpus for merging term variants in canonical lemmatized forms. Conflation performance was evaluated in terms of an adaptation of recall and precision measures, based on accuracy and coverage, not actual retrieval. The results were compared with those obtained using a Spanish version of the Porter algorithm. Findings - The conclusion is that the main strength of lemmatization is its accuracy, whereas its main limitation is the underanalysis of variant forms. Originality/value - The report outlines the potential of transducers in their application to normalization processes.

0.0014978976 = product of:
  0.020970564 = sum of:
    0.020970564 = weight(_text_:retrieval in 2314) [ClassicSimilarity], result of:
      0.020970564 = score(doc=2314,freq=2.0), product of:
        0.08963835 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.029633347 = queryNorm
        0.23394634 = fieldWeight in 2314, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0546875 = fieldNorm(doc=2314)
  0.071428575 = coord(1/14)

Abstract

Corpus linguistics is the study of large, computer held bodies of text. Some corpus linguists are concerned with language descriptions for its own sake. On the corpus-linguistic continuum, the study of raw ASCII text is situated at one end, and the study of heavily pre-coded text at the other. Discusses the use of word frequency to identify changes in the lexicon; word repetition and word positioning in automatic abstracting and word clusters in automatic text retrieval. Compares the machine extract with manual abstracts. Abstractors and indexers may find themselves taking the original wording of the text more into account as the focus moves towards the electronic medium and away from the hard copy

0.0012839122 = product of:
  0.01797477 = sum of:
    0.01797477 = weight(_text_:retrieval in 4476) [ClassicSimilarity], result of:
      0.01797477 = score(doc=4476,freq=2.0), product of:
        0.08963835 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.029633347 = queryNorm
        0.20052543 = fieldWeight in 4476, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.046875 = fieldNorm(doc=4476)
  0.071428575 = coord(1/14)

Abstract

This paper presents a morphological classification of languages from the IR perspective. Linguistic typology research has shown that the morphological complexity of every language in the world can be described by two variables, index of synthesis and index of fusion. These variables provide a theoretical basis for IR research handling morphological issues. A common theoretical framework is needed in particular because of the increasing significance of cross-language retrieval research and CLIR systems processing different languages. The paper elaborates the linguistic morphological typology for the purposes of IR research. It studies how the indexes of synthesis and fusion could be used as practical tools in mono- and cross-lingual IR research. The need for semantic and syntactic typologies is discussed. The paper also reviews studies made in different languages on the effects of morphology and stemming in IR.

8.826613E-4 = product of:
  0.012357258 = sum of:
    0.012357258 = weight(_text_:information in 6029) [ClassicSimilarity], result of:
      0.012357258 = score(doc=6029,freq=12.0), product of:
        0.052020688 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.029633347 = queryNorm
        0.23754507 = fieldWeight in 6029, product of:
          3.4641016 = tf(freq=12.0), with freq of:
            12.0 = termFreq=12.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0390625 = fieldNorm(doc=6029)
  0.071428575 = coord(1/14)

Abstract

Over the past few years, temporal information processing and temporal database management have increasingly become hot topics. Nevertheless, only a few researchers have investigated these areas in the Chinese language. This lays down the objective of our research: to exploit Chinese language processing techniques for temporal information extraction and concept reasoning. In this article, we first study the mechanism for expressing time in Chinese. On the basis of the study, we then design a general frame structure for maintaining the extracted temporal concepts and propose a system for extracting time-dependent information from Hong Kong financial news. In the system, temporal knowledge is represented by different types of temporal concepts (TTC) and different temporal relations, including absolute and relative relations, which are used to correlate between action times and reference times. In analyzing a sentence, the algorithm first determines the situation related to the verb. This in turn will identify the type of temporal concept associated with the verb. After that, the relevant temporal information is extracted and the temporal relations are derived. These relations link relevant concept frames together in chronological order, which in turn provide the knowledge to fulfill users' queries, e.g., for question-answering (i.e., Q&A) applications

Source

Journal of the American Society for Information Science and technology. 52(2001) no.9, S.748-762

5.7655195E-4 = product of:
  0.008071727 = sum of:
    0.008071727 = weight(_text_:information in 6682) [ClassicSimilarity], result of:
      0.008071727 = score(doc=6682,freq=2.0), product of:
        0.052020688 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.029633347 = queryNorm
        0.1551638 = fieldWeight in 6682, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0625 = fieldNorm(doc=6682)
  0.071428575 = coord(1/14)

Source

Information processing and management. 27(1991) nos.2/3, S.145-151

5.04483E-4 = product of:
  0.0070627616 = sum of:
    0.0070627616 = weight(_text_:information in 6681) [ClassicSimilarity], result of:
      0.0070627616 = score(doc=6681,freq=2.0), product of:
        0.052020688 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.029633347 = queryNorm
        0.13576832 = fieldWeight in 6681, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0546875 = fieldNorm(doc=6681)
  0.071428575 = coord(1/14)

Source

Information processing and management. 27(1991) no.1, S.43-54