Search (28 results, page 1 of 2)

0.13563867 = product of:
  0.33909667 = sum of:
    0.2896754 = weight(_text_:3a in 562) [ClassicSimilarity], result of:
      0.2896754 = score(doc=562,freq=2.0), product of:
        0.51542014 = queryWeight, product of:
          8.478011 = idf(docFreq=24, maxDocs=44218)
          0.060794935 = queryNorm
        0.56201804 = fieldWeight in 562, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          8.478011 = idf(docFreq=24, maxDocs=44218)
          0.046875 = fieldNorm(doc=562)
    0.049421243 = weight(_text_:22 in 562) [ClassicSimilarity], result of:
      0.049421243 = score(doc=562,freq=2.0), product of:
        0.2128935 = queryWeight, product of:
          3.5018296 = idf(docFreq=3622, maxDocs=44218)
          0.060794935 = queryNorm
        0.23214069 = fieldWeight in 562, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.5018296 = idf(docFreq=3622, maxDocs=44218)
          0.046875 = fieldNorm(doc=562)
  0.4 = coord(2/5)

Content

Vgl.: http://www.google.de/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&ved=0CEAQFjAA&url=http%3A%2F%2Fciteseerx.ist.psu.edu%2Fviewdoc%2Fdownload%3Fdoi%3D10.1.1.91.4940%26rep%3Drep1%26type%3Dpdf&ei=dOXrUMeIDYHDtQahsIGACg&usg=AFQjCNHFWVh6gNPvnOrOS9R3rkrXCNVD-A&sig2=5I2F5evRfMnsttSgFF9g7Q&bvm=bv.1357316858,d.Yms.

Date

8. 1.2013 10:22:32

0.032073338 = product of:
  0.16036668 = sum of:
    0.16036668 = weight(_text_:thesaurus in 4775) [ClassicSimilarity], result of:
      0.16036668 = score(doc=4775,freq=10.0), product of:
        0.28093818 = queryWeight, product of:
          4.6210785 = idf(docFreq=1182, maxDocs=44218)
          0.060794935 = queryNorm
        0.5708255 = fieldWeight in 4775, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          4.6210785 = idf(docFreq=1182, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4775)
  0.2 = coord(1/5)

Abstract

The progressive increase of information content has recently made it necessary to create a system for automatic classification of documents. In this article, a system is presented for the categorization of multiclass Farsi documents that requires fewer training examples and can help to compensate the shortcoming of the standard training dataset. The new idea proposed in the present article is based on extending the feature vector by adding some words extracted from a thesaurus and then filtering the new feature vector by applying secondary feature selection to discard inappropriate features. In fact, a phase of secondary feature selection is applied to choose more appropriate features among the features added from a thesaurus to enhance the effect of using a thesaurus on the efficiency of the classifier. To evaluate the proposed system, a corpus is gathered from the Farsi Wikipedia website and some articles in the Hamshahri newspaper, the Roshd periodical, and the Soroush magazine. In addition to studying the role of a thesaurus and applying secondary feature selection, the effect of a various number of categories, size of the training dataset, and average number of words in the test data also are examined. As the results indicate, classification efficiency improves by applying this approach, especially when available data is not sufficient for some text categories.

0.026835246 = product of:
  0.13417622 = sum of:
    0.13417622 = weight(_text_:objects in 2836) [ClassicSimilarity], result of:
      0.13417622 = score(doc=2836,freq=4.0), product of:
        0.3231294 = queryWeight, product of:
          5.315071 = idf(docFreq=590, maxDocs=44218)
          0.060794935 = queryNorm
        0.41523993 = fieldWeight in 2836, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          5.315071 = idf(docFreq=590, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2836)
  0.2 = coord(1/5)

Abstract

Classification is the process of assigning objects to pre-defined classes based on observations or characteristics of those objects, and there are many approaches to performing this task. The overall objective of this study is to demonstrate the use of two learning techniques to analyze the results of a manual classification system. Our sample consisted of 1,026 documents, from the ACM Computing Classification System, classified by their authors as belonging to one of the groups of the classification system: "H.3 Information Storage and Retrieval." A singular value decomposition of the documents' weighted term-frequency matrix was used to represent each document in a 50-dimensional vector space. The analysis of the representation using both supervised (decision tree) and unsupervised (clustering) techniques suggests that two pairs of the ACM classes are closely related to each other in the vector space. Class 1 (Content Analysis and Indexing) is closely related to Class 3 (Information Search and Retrieval), and Class 4 (Systems and Software) is closely related to Class 5 (Online Information Services). Further analysis was performed to test the diffusion of the words in the two classes using both cosine and Euclidean distance.

0.022949811 = product of:
  0.11474905 = sum of:
    0.11474905 = weight(_text_:thesaurus in 1667) [ClassicSimilarity], result of:
      0.11474905 = score(doc=1667,freq=2.0), product of:
        0.28093818 = queryWeight, product of:
          4.6210785 = idf(docFreq=1182, maxDocs=44218)
          0.060794935 = queryNorm
        0.40844947 = fieldWeight in 1667, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.6210785 = idf(docFreq=1182, maxDocs=44218)
          0.0625 = fieldNorm(doc=1667)
  0.2 = coord(1/5)

Content

1 Introduction / 2 Method overview / 3 Ei thesaurus preprocessing / 4 Automatic classification process: 4.1 Matching -- 4.2 Weighting -- 4.3 Preparation for display / 5 Results of the classification process / 6 Evaluations / 7 Software / 8 Other applications / 9 Experiments with universal classification systems / References / Appendix A: Ei classification service: Software / Appendix B: Use of the classification software as subject filter in a WWW harvester.

0.020081086 = product of:
  0.100405425 = sum of:
    0.100405425 = weight(_text_:thesaurus in 1568) [ClassicSimilarity], result of:
      0.100405425 = score(doc=1568,freq=2.0), product of:
        0.28093818 = queryWeight, product of:
          4.6210785 = idf(docFreq=1182, maxDocs=44218)
          0.060794935 = queryNorm
        0.3573933 = fieldWeight in 1568, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.6210785 = idf(docFreq=1182, maxDocs=44218)
          0.0546875 = fieldNorm(doc=1568)
  0.2 = coord(1/5)

Abstract

Emerging standards in knowledge representation and organization are preparing the way for distributed vocabulary support in Internet search services. NetLab researchers are exploring several innovative solutions for searching and browsing in the subject-based Internet gateway, Electronic Engineering Library, Sweden (EELS). The implementation of the EELS service is described, specifically, the generation of the robot-gathered database 'All' engineering and the automated application of the Ei thesaurus and classification scheme. NetLab and OCLC researchers are collaborating to investigate advanced solutions to automated classification in the DESIRE II context. A plan for furthering the development of distributed vocabulary support in Internet search services is offered.

0.019768497 = product of:
  0.09884249 = sum of:
    0.09884249 = weight(_text_:22 in 1046) [ClassicSimilarity], result of:
      0.09884249 = score(doc=1046,freq=2.0), product of:
        0.2128935 = queryWeight, product of:
          3.5018296 = idf(docFreq=3622, maxDocs=44218)
          0.060794935 = queryNorm
        0.46428138 = fieldWeight in 1046, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.5018296 = idf(docFreq=3622, maxDocs=44218)
          0.09375 = fieldNorm(doc=1046)
  0.2 = coord(1/5)

Date

5. 5.2003 14:17:22

0.018975385 = product of:
  0.09487692 = sum of:
    0.09487692 = weight(_text_:objects in 2496) [ClassicSimilarity], result of:
      0.09487692 = score(doc=2496,freq=2.0), product of:
        0.3231294 = queryWeight, product of:
          5.315071 = idf(docFreq=590, maxDocs=44218)
          0.060794935 = queryNorm
        0.29361898 = fieldWeight in 2496, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          5.315071 = idf(docFreq=590, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2496)
  0.2 = coord(1/5)

Abstract

Descriptive document clustering aims at discovering clusters of semantically interrelated documents together with meaningful labels to summarize the content of each document cluster. In this work, we propose a novel descriptive clustering framework, referred to as CEDL. It relies on the formulation and generation of 2 types of heterogeneous objects, which correspond to documents and candidate phrases, using multilevel similarity information. CEDL is composed of 5 main processing stages. First, it simultaneously maps the documents and candidate phrases into a common co-embedded space that preserves higher-order, neighbor-based proximities between the combined sets of documents and phrases. Then, it discovers an approximate cluster structure of documents in the common space. The third stage extracts promising topic phrases by constructing a discriminant model where documents along with their cluster memberships are used as training instances. Subsequently, the final cluster labels are selected from the topic phrases using a ranking scheme using multiple scores based on the extracted co-embedding information and the discriminant output. The final stage polishes the initial clusters to reduce noise and accommodate the multitopic nature of documents. The effectiveness and competitiveness of CEDL is demonstrated qualitatively and quantitatively with experiments using document databases from different application fields.

0.01721236 = product of:
  0.08606179 = sum of:
    0.08606179 = weight(_text_:thesaurus in 5897) [ClassicSimilarity], result of:
      0.08606179 = score(doc=5897,freq=2.0), product of:
        0.28093818 = queryWeight, product of:
          4.6210785 = idf(docFreq=1182, maxDocs=44218)
          0.060794935 = queryNorm
        0.30633712 = fieldWeight in 5897, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.6210785 = idf(docFreq=1182, maxDocs=44218)
          0.046875 = fieldNorm(doc=5897)
  0.2 = coord(1/5)

Abstract

The primary objective of this study was to identify and address problems of applying a controlled vocabulary in automated subject classification of textual Web pages, in the area of engineering. Web pages have special characteristics such as structural information, but are at the same time rather heterogeneous. The classification approach used comprises string-to-string matching between words in a term list extracted from the Ei (Engineering Information) thesaurus and classification scheme, and words in the text to be classified. Based on a sample of 70 Web pages, a number of problems with the term list are identified. Reasons for those problems are discussed and improvements proposed. Methods for implementing the improvements are also specified, suggesting further research.

0.01721236 = product of:
  0.08606179 = sum of:
    0.08606179 = weight(_text_:thesaurus in 1461) [ClassicSimilarity], result of:
      0.08606179 = score(doc=1461,freq=2.0), product of:
        0.28093818 = queryWeight, product of:
          4.6210785 = idf(docFreq=1182, maxDocs=44218)
          0.060794935 = queryNorm
        0.30633712 = fieldWeight in 1461, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.6210785 = idf(docFreq=1182, maxDocs=44218)
          0.046875 = fieldNorm(doc=1461)
  0.2 = coord(1/5)

Abstract

Automated subject classification has been a challenging research issue for many years now, receiving particular attention in the past decade due to rapid increase of digital documents. The most frequent approach to automated classification is machine learning. It, however, requires training documents and performs well on new documents only if these are similar enough to the former. We explore a string-matching algorithm based on a controlled vocabulary, which does not require training documents - instead it reuses the intellectual work put into creating the controlled vocabulary. Terms from the Engineering Information thesaurus and classification scheme were matched against title and abstract of engineering papers from the Compendex database. Simple string-matching was enhanced by several methods such as term weighting schemes and cut-offs, exclusion of certain terms, and en- richment of the controlled vocabulary with automatically extracted terms. The best results are 76% recall when the controlled vocabulary is enriched with new terms, and 79% precision when certain terms are excluded. Precision of individual classes is up to 98%. These results are comparable to state-of-the-art machine-learning algorithms.

0.01647375 = product of:
  0.08236875 = sum of:
    0.08236875 = weight(_text_:22 in 611) [ClassicSimilarity], result of:
      0.08236875 = score(doc=611,freq=2.0), product of:
        0.2128935 = queryWeight, product of:
          3.5018296 = idf(docFreq=3622, maxDocs=44218)
          0.060794935 = queryNorm
        0.38690117 = fieldWeight in 611, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.5018296 = idf(docFreq=3622, maxDocs=44218)
          0.078125 = fieldNorm(doc=611)
  0.2 = coord(1/5)

Date

22. 8.2009 12:54:24

0.01647375 = product of:
  0.08236875 = sum of:
    0.08236875 = weight(_text_:22 in 2748) [ClassicSimilarity], result of:
      0.08236875 = score(doc=2748,freq=2.0), product of:
        0.2128935 = queryWeight, product of:
          3.5018296 = idf(docFreq=3622, maxDocs=44218)
          0.060794935 = queryNorm
        0.38690117 = fieldWeight in 2748, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.5018296 = idf(docFreq=3622, maxDocs=44218)
          0.078125 = fieldNorm(doc=2748)
  0.2 = coord(1/5)

Date

1. 2.2016 18:25:22

0.016227966 = product of:
  0.08113983 = sum of:
    0.08113983 = weight(_text_:thesaurus in 4051) [ClassicSimilarity], result of:
      0.08113983 = score(doc=4051,freq=4.0), product of:
        0.28093818 = queryWeight, product of:
          4.6210785 = idf(docFreq=1182, maxDocs=44218)
          0.060794935 = queryNorm
        0.2888174 = fieldWeight in 4051, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          4.6210785 = idf(docFreq=1182, maxDocs=44218)
          0.03125 = fieldNorm(doc=4051)
  0.2 = coord(1/5)

Abstract

Mit der Anfang 2010 begonnen Implementierung und Ergebnisevaluierung des automatischen Indexierungsverfahrens "Decisiv Categorization" der Firma Recommind soll das hier skizzierte Informationsstrukturierungsproblem in zwei Schritten gelöst werden. Kurz- bis mittelfristig soll die intellektuelle Indexierung durch ein semiautomatisches Verfahren6 unterstützt werden. Mittel- bis langfristig soll das maschinelle Verfahren, aufbauend auf einem entsprechenden Training, in die Lage versetzt werden, sowohl im Hause vorliegende Dokumente vollautomatisch zu indexieren als auch ZBW-fremde digitale Informationsressourcen zu verschlagworten bzw. zu klassifizieren, um sie in einem gemeinsamen Suchraum auffindbar machen zu können. Im Anschluss an diese Einleitung werden die ersten Ansätze maschineller Sacherschließung an der ZBW (2001-2004) und deren Ergebnisse und Problemlagen aufgezeigt. Danach werden die Rahmenbedingungen (Projektauftrag und -ziel) für eine Wiederaufnahme des Vorhabens im Jahre 2009 aufgezeigt, gefolgt von einer Darstellung der Funktionsweise der Recommind-Technologie und deren Einsatz im Rahmen der Sacherschließung von Online-Dokumenten mit einem Thesaurus. Schwerpunkt dieser Abhandlung bilden im Anschluss daran die Evaluierungsmöglichkeiten automatischer Indexierungsansätze sowie die aktuellen Ergebnisse und zentralen Erkenntnisse des Einsatzes im Kontext der ZBW. Das Fazit beschreibt die entsprechenden Schlussfolgerungen aus den erzielten Ergebnissen sowie den Ausblick auf das weitere Vorgehen.

Object

Standard-Thesaurus Wirtschaft

0.014343631 = product of:
  0.07171816 = sum of:
    0.07171816 = weight(_text_:thesaurus in 5769) [ClassicSimilarity], result of:
      0.07171816 = score(doc=5769,freq=2.0), product of:
        0.28093818 = queryWeight, product of:
          4.6210785 = idf(docFreq=1182, maxDocs=44218)
          0.060794935 = queryNorm
        0.2552809 = fieldWeight in 5769, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.6210785 = idf(docFreq=1182, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5769)
  0.2 = coord(1/5)

Abstract

Statistical association measures have been widely applied in information retrieval research, usually employing a clustering of documents or terms on the basis of their relationships. Applications of the association measures for term clustering include automatic thesaurus construction and query expansion. This research evaluates the similarity of six association measures by comparing the relationship and behavior they demonstrate in various analyses of a test corpus. Analysis techniques include comparisons of highly ranked term pairs and term clusters, analyses of the correlation among the association measures using Pearson's correlation coefficient and MDS mapping, and an analysis of the impact of a term frequency on the association values by means of z-score. The major findings of the study are as follows: First, the most similar association measures are mutual information and Yule's coefficient of colligation Y, whereas cosine and Jaccard coefficients, as well as X**2 statistic and likelihood ratio, demonstrate quite similar behavior for terms with high frequency. Second, among all the measures, the X**2 statistic is the least affected by the frequency of terms. Third, although cosine and Jaccard coefficients tend to emphasize high frequency terms, mutual information and Yule's Y seem to overestimate rare terms

0.014343631 = product of:
  0.07171816 = sum of:
    0.07171816 = weight(_text_:thesaurus in 472) [ClassicSimilarity], result of:
      0.07171816 = score(doc=472,freq=2.0), product of:
        0.28093818 = queryWeight, product of:
          4.6210785 = idf(docFreq=1182, maxDocs=44218)
          0.060794935 = queryNorm
        0.2552809 = fieldWeight in 472, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.6210785 = idf(docFreq=1182, maxDocs=44218)
          0.0390625 = fieldNorm(doc=472)
  0.2 = coord(1/5)

Abstract

The following paper deals with an automatic text classification method which does not require training documents. For this method the German Subject Heading Authority File (SWD), provided by the linked data service of the German National Library is used. Recently the SWD was enriched with notations of the Dewey Decimal Classification (DDC). In consequence it became possible to utilize the subject headings as textual representations for the notations of the DDC. Basically, we we derive the classification of a text from the classification of the words in the text given by the thesaurus. The method was tested by classifying 3826 OAI-Records from 7 different repositories. Mean reciprocal rank and recall were chosen as evaluation measure. Direct comparison to a machine learning method has shown that this method is definitely competitive. Thus we can conclude that the enriched version of the SWD provides high quality information with a broad coverage for classification of German scientific articles.

0.011531624 = product of:
  0.05765812 = sum of:
    0.05765812 = weight(_text_:22 in 141) [ClassicSimilarity], result of:
      0.05765812 = score(doc=141,freq=2.0), product of:
        0.2128935 = queryWeight, product of:
          3.5018296 = idf(docFreq=3622, maxDocs=44218)
          0.060794935 = queryNorm
        0.2708308 = fieldWeight in 141, 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=141)
  0.2 = coord(1/5)

Pages

S.1-22

0.011531624 = product of:
  0.05765812 = sum of:
    0.05765812 = weight(_text_:22 in 2338) [ClassicSimilarity], result of:
      0.05765812 = score(doc=2338,freq=2.0), product of:
        0.2128935 = queryWeight, product of:
          3.5018296 = idf(docFreq=3622, maxDocs=44218)
          0.060794935 = queryNorm
        0.2708308 = fieldWeight in 2338, 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=2338)
  0.2 = coord(1/5)

Date

22. 9.1997 19:16:05

0.011531624 = product of:
  0.05765812 = sum of:
    0.05765812 = weight(_text_:22 in 1563) [ClassicSimilarity], result of:
      0.05765812 = score(doc=1563,freq=2.0), product of:
        0.2128935 = queryWeight, product of:
          3.5018296 = idf(docFreq=3622, maxDocs=44218)
          0.060794935 = queryNorm
        0.2708308 = fieldWeight in 1563, 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=1563)
  0.2 = coord(1/5)

Date

5. 5.2003 9:22:09

0.011531624 = product of:
  0.05765812 = sum of:
    0.05765812 = weight(_text_:22 in 1673) [ClassicSimilarity], result of:
      0.05765812 = score(doc=1673,freq=2.0), product of:
        0.2128935 = queryWeight, product of:
          3.5018296 = idf(docFreq=3622, maxDocs=44218)
          0.060794935 = queryNorm
        0.2708308 = fieldWeight in 1673, 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=1673)
  0.2 = coord(1/5)

Date

1. 8.1996 22:08:06

0.011531624 = product of:
  0.05765812 = sum of:
    0.05765812 = weight(_text_:22 in 5273) [ClassicSimilarity], result of:
      0.05765812 = score(doc=5273,freq=2.0), product of:
        0.2128935 = queryWeight, product of:
          3.5018296 = idf(docFreq=3622, maxDocs=44218)
          0.060794935 = queryNorm
        0.2708308 = fieldWeight in 5273, 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=5273)
  0.2 = coord(1/5)

Date

22. 7.2006 16:24:52

0.011531624 = product of:
  0.05765812 = sum of:
    0.05765812 = weight(_text_:22 in 2560) [ClassicSimilarity], result of:
      0.05765812 = score(doc=2560,freq=2.0), product of:
        0.2128935 = queryWeight, product of:
          3.5018296 = idf(docFreq=3622, maxDocs=44218)
          0.060794935 = queryNorm
        0.2708308 = fieldWeight in 2560, 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=2560)
  0.2 = coord(1/5)

Date

22. 9.2008 18:31:54