Search (13 results, page 1 of 1)

0.048820432 = product of:
  0.097640865 = sum of:
    0.051698197 = weight(_text_:digital in 1071) [ClassicSimilarity], result of:
      0.051698197 = score(doc=1071,freq=2.0), product of:
        0.19770671 = queryWeight, product of:
          3.944552 = idf(docFreq=2326, maxDocs=44218)
          0.050121464 = queryNorm
        0.26148933 = fieldWeight in 1071, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.944552 = idf(docFreq=2326, maxDocs=44218)
          0.046875 = fieldNorm(doc=1071)
    0.045942668 = weight(_text_:library in 1071) [ClassicSimilarity], result of:
      0.045942668 = score(doc=1071,freq=8.0), product of:
        0.1317883 = queryWeight, product of:
          2.6293786 = idf(docFreq=8668, maxDocs=44218)
          0.050121464 = queryNorm
        0.34860963 = fieldWeight in 1071, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          2.6293786 = idf(docFreq=8668, maxDocs=44218)
          0.046875 = fieldNorm(doc=1071)
  0.5 = coord(2/4)

Abstract

This paper aims to provide an overview of automatic classification research, which focuses on issues related to the automatic classification of documents in a library environment. The review covers literature published in mainstream library and information science studies. The review was done on literature published in both academic and professional LIS journals and other documents. This review reveals that basically three types of research are being done on automatic classification: 1) hierarchical classification using different library classification schemes, 2) text categorization and document categorization using different type of classifiers with or without using training documents, and 3) automatic bibliographic classification. Predominantly this research is directed towards solving problems of organization of digital documents in an online environment. However, very little research is devoted towards solving the problems of arrangement of physical documents.

0.040034845 = product of:
  0.08006969 = sum of:
    0.060926907 = weight(_text_:digital in 472) [ClassicSimilarity], result of:
      0.060926907 = score(doc=472,freq=4.0), product of:
        0.19770671 = queryWeight, product of:
          3.944552 = idf(docFreq=2326, maxDocs=44218)
          0.050121464 = queryNorm
        0.3081681 = fieldWeight in 472, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.944552 = idf(docFreq=2326, maxDocs=44218)
          0.0390625 = fieldNorm(doc=472)
    0.01914278 = weight(_text_:library in 472) [ClassicSimilarity], result of:
      0.01914278 = score(doc=472,freq=2.0), product of:
        0.1317883 = queryWeight, product of:
          2.6293786 = idf(docFreq=8668, maxDocs=44218)
          0.050121464 = queryNorm
        0.14525402 = fieldWeight in 472, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          2.6293786 = idf(docFreq=8668, maxDocs=44218)
          0.0390625 = fieldNorm(doc=472)
  0.5 = coord(2/4)

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.

Source

Proceedings of the 2nd International Workshop on Semantic Digital Archives held in conjunction with the 16th Int. Conference on Theory and Practice of Digital Libraries (TPDL) on September 27, 2012 in Paphos, Cyprus [http://ceur-ws.org/Vol-912/proceedings.pdf]. Eds.: A. Mitschik et al

0.035819627 = product of:
  0.14327851 = sum of:
    0.14327851 = sum of:
      0.10253391 = weight(_text_:project in 2158) [ClassicSimilarity], result of:
        0.10253391 = score(doc=2158,freq=6.0), product of:
          0.21156175 = queryWeight, product of:
            4.220981 = idf(docFreq=1764, maxDocs=44218)
            0.050121464 = queryNorm
          0.48465237 = fieldWeight in 2158, product of:
            2.4494898 = tf(freq=6.0), with freq of:
              6.0 = termFreq=6.0
            4.220981 = idf(docFreq=1764, maxDocs=44218)
            0.046875 = fieldNorm(doc=2158)
      0.0407446 = weight(_text_:22 in 2158) [ClassicSimilarity], result of:
        0.0407446 = score(doc=2158,freq=2.0), product of:
          0.17551683 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.050121464 = queryNorm
          0.23214069 = fieldWeight in 2158, 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=2158)
  0.25 = coord(1/4)

Abstract

This paper introduces a project to develop a reliable, cost-effective method for classifying Internet texts into register categories, and apply that approach to the analysis of a large corpus of web documents. To date, the project has proceeded in 2 key phases. First, we developed a bottom-up method for web register classification, asking end users of the web to utilize a decision-tree survey to code relevant situational characteristics of web documents, resulting in a bottom-up identification of register and subregister categories. We present details regarding the development and testing of this method through a series of 10 pilot studies. Then, in the second phase of our project we applied this procedure to a corpus of 53,000 web documents. An analysis of the results demonstrates the effectiveness of these methods for web register classification and provides a preliminary description of the types and distribution of registers on the web.

Date

4. 8.2015 19:22:04

0.01865498 = product of:
  0.07461992 = sum of:
    0.07461992 = weight(_text_:digital in 1056) [ClassicSimilarity], result of:
      0.07461992 = score(doc=1056,freq=6.0), product of:
        0.19770671 = queryWeight, product of:
          3.944552 = idf(docFreq=2326, maxDocs=44218)
          0.050121464 = queryNorm
        0.37742734 = fieldWeight in 1056, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.944552 = idf(docFreq=2326, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1056)
  0.25 = coord(1/4)

Abstract

The ever increasing amount of digitally available information is curse and blessing at the same time. On the one hand, users have increasingly large amounts of information at their fingertips. On the other hand, the assessment and refinement of web search results becomes more and more tiresome and difficult for non-experts in a domain. Therefore, established digital libraries offer specialized collections with a certain degree of quality. This quality can largely be attributed to the great effort invested into semantic enrichment of the provided documents e.g. by annotating their documents with respect to a domain-specific taxonomy. This process is still done manually in many domains, e.g. chemistry CAS, medicine MeSH, or mathematics MSC. But due to the growing amount of data, this manual task gets more and more time consuming and expensive. The only solution for this problem seems to employ automated classification algorithms, but from evaluations done in previous research, conclusions to a real world scenario are difficult to make. We therefore conducted a large scale feasibility study on a real world data set from one of the biggest mathematical digital libraries, i.e. Zentralblatt MATH, with special focus on its practical applicability.

Source

15th International Conference on Asia-Pacific Digital Libraries ICADL 2013. Bangalore, India. [to appear, 2013]

0.012924549 = product of:
  0.051698197 = sum of:
    0.051698197 = weight(_text_:digital in 2470) [ClassicSimilarity], result of:
      0.051698197 = score(doc=2470,freq=2.0), product of:
        0.19770671 = queryWeight, product of:
          3.944552 = idf(docFreq=2326, maxDocs=44218)
          0.050121464 = queryNorm
        0.26148933 = fieldWeight in 2470, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.944552 = idf(docFreq=2326, maxDocs=44218)
          0.046875 = fieldNorm(doc=2470)
  0.25 = coord(1/4)

Abstract

Das sprunghafte Anwachsen der Menge digital verfügbarer Dokumente gepaart mit dem Zeit- und Personalmangel an wissenschaftlichen Bibliotheken legt den Einsatz von halb- oder vollautomatischen Verfahren für die verbale und klassifikatorische Inhaltserschließung nahe. Nach einer kurzen allgemeinen Einführung in die gängige Methodik beleuchtet dieser Artikel eine Reihe von Projekten zur automatisierten Klassifizierung aus dem Zeitraum 2007-2012 und aus dem deutschsprachigen Raum. Ein Großteil der vorgestellten Projekte verwendet Methoden des Maschinellen Lernens aus der Künstlichen Intelligenz, arbeitet meist mit angepassten Versionen einer kommerziellen Software und bezieht sich in der Regel auf die Dewey Decimal Classification (DDC). Als Datengrundlage dienen Metadatensätze, Abstracs, Inhaltsverzeichnisse und Volltexte in diversen Datenformaten. Die abschließende Analyse enthält eine Anordnung der Projekte nach einer Reihe von verschiedenen Kriterien und eine Zusammenfassung der aktuellen Lage und der größten Herausfordungen für automatisierte Klassifizierungsverfahren.

0.012924549 = product of:
  0.051698197 = sum of:
    0.051698197 = weight(_text_:digital in 3015) [ClassicSimilarity], result of:
      0.051698197 = score(doc=3015,freq=2.0), product of:
        0.19770671 = queryWeight, product of:
          3.944552 = idf(docFreq=2326, maxDocs=44218)
          0.050121464 = queryNorm
        0.26148933 = fieldWeight in 3015, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.944552 = idf(docFreq=2326, maxDocs=44218)
          0.046875 = fieldNorm(doc=3015)
  0.25 = coord(1/4)

Abstract

We analyze the linguistic evolution of selected scientific disciplines over a 30-year time span (1970s to 2000s). Our focus is on four highly specialized disciplines at the boundaries of computer science that emerged during that time: computational linguistics, bioinformatics, digital construction, and microelectronics. Our analysis is driven by the question whether these disciplines develop a distinctive language use-both individually and collectively-over the given time period. The data set is the English Scientific Text Corpus (scitex), which includes texts from the 1970s/1980s and early 2000s. Our theoretical basis is register theory. In terms of methods, we combine corpus-based methods of feature extraction (various aggregated features [part-of-speech based], n-grams, lexico-grammatical patterns) and automatic text classification. The results of our research are directly relevant to the study of linguistic variation and languages for specific purposes (LSP) and have implications for various natural language processing (NLP) tasks, for example, authorship attribution, text mining, or training NLP tools.

0.010464822 = product of:
  0.041859288 = sum of:
    0.041859288 = product of:
      0.083718576 = sum of:
        0.083718576 = weight(_text_:project in 1057) [ClassicSimilarity], result of:
          0.083718576 = score(doc=1057,freq=4.0), product of:
            0.21156175 = queryWeight, product of:
              4.220981 = idf(docFreq=1764, maxDocs=44218)
              0.050121464 = queryNorm
            0.39571697 = fieldWeight in 1057, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              4.220981 = idf(docFreq=1764, maxDocs=44218)
              0.046875 = fieldNorm(doc=1057)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

Abstract

In this document we describe the final release of the toolset for entity and semantic associations, integrating two versions (language dependent and language independent) of Unsupervised Document Similarity implemented by MU (using gensim tool) and Citation Indexing, Resolution and Matching (UJF/CMD). We give a brief description of tools, the rationale behind decisions made, and provide elementary evaluation. Tools are integrated in the main project result, EuDML website, and they deliver the needed functionality for exploratory searching and browsing the collected documents. EuDML users and content providers thus benefit from millions of algorithmically generated similarity and citation links, developed using state of the art machine learning and matching methods.

Content

Vgl. auch: https://is.muni.cz/repo/1076213/en/Lee-Sojka-Rehurek-Bolikowski/Toolset-for-Entity-and-Semantic-Associations-Initial-Release-Deliverable-82-of-project-EuDML?lang=en.

0.008488459 = product of:
  0.033953834 = sum of:
    0.033953834 = product of:
      0.06790767 = sum of:
        0.06790767 = weight(_text_:22 in 2748) [ClassicSimilarity], result of:
          0.06790767 = score(doc=2748,freq=2.0), product of:
            0.17551683 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.050121464 = 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)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

Date

1. 2.2016 18:25:22

0.0061664553 = product of:
  0.024665821 = sum of:
    0.024665821 = product of:
      0.049331643 = sum of:
        0.049331643 = weight(_text_:project in 238) [ClassicSimilarity], result of:
          0.049331643 = score(doc=238,freq=2.0), product of:
            0.21156175 = queryWeight, product of:
              4.220981 = idf(docFreq=1764, maxDocs=44218)
              0.050121464 = queryNorm
            0.23317845 = fieldWeight in 238, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.220981 = idf(docFreq=1764, maxDocs=44218)
              0.0390625 = fieldNorm(doc=238)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

Abstract

This paper presents a two-phased research project aiming to improve email triage for public administration managers. The first phase developed a typology of email classification patterns through a qualitative study involving 34 participants. Inspired by the fields of pragmatics and speech act theory, this typology comprising four top level categories and 13 subcategories represents the typical email triage behaviors of managers in an organizational context. The second study phase was conducted on a corpus of 1,703 messages using email samples of two managers. Using the k-NN (k-nearest neighbor) algorithm, statistical treatments automatically classified the email according to lexical and nonlexical features representative of managers' triage patterns. The automatic classification of email according to the lexicon of the messages was found to be substantially more efficient when k = 2 and n = 2,000. For four categories, the average recall rate was 94.32%, the average precision rate was 94.50%, and the accuracy rate was 94.54%. For 13 categories, the average recall rate was 91.09%, the average precision rate was 84.18%, and the accuracy rate was 88.70%. It appears that a message's nonlexical features are also deeply influenced by email pragmatics. Features related to the recipient and the sender were the most relevant for characterizing email.

0.0054143956 = product of:
  0.021657582 = sum of:
    0.021657582 = weight(_text_:library in 2301) [ClassicSimilarity], result of:
      0.021657582 = score(doc=2301,freq=4.0), product of:
        0.1317883 = queryWeight, product of:
          2.6293786 = idf(docFreq=8668, maxDocs=44218)
          0.050121464 = queryNorm
        0.16433616 = fieldWeight in 2301, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          2.6293786 = idf(docFreq=8668, maxDocs=44218)
          0.03125 = fieldNorm(doc=2301)
  0.25 = coord(1/4)

Abstract

Analytico-synthetic and faceted classifications, such as Universal Decimal Classification (UDC) express content of documents with complex, pre-combined classification codes. Without classification authority control that would help manage and access structured notations, the use of UDC codes in searching and browsing is limited. Existing UDC parsing solutions are usually created for a particular database system or a specific task and are not widely applicable. The approach described in this paper provides a solution by which the analysis and interpretation of UDC notations would be stored into an intermediate format (in this case, in XML) by automatic means without any data or information loss. Due to its richness, the output file can be converted into different formats, such as standard mark-up and data exchange formats or simple lists of the recommended entry points of a UDC number. The program can also be used to create authority records containing complex UDC numbers which can be comprehensively analysed in order to be retrieved effectively. The Java program, as well as the corresponding schema definition it employs, is under continuous development. The current version of the interpreter software is now available online for testing purposes at the following web site: http://interpreter-eto.rhcloud.com. The future plan is to implement conversion methods for standard formats and to create standard online interfaces in order to make it possible to use the features of software as a service. This would result in the algorithm being able to be employed both in existing and future library systems to analyse UDC numbers without any significant programming effort.

0.005093075 = product of:
  0.0203723 = sum of:
    0.0203723 = product of:
      0.0407446 = sum of:
        0.0407446 = weight(_text_:22 in 690) [ClassicSimilarity], result of:
          0.0407446 = score(doc=690,freq=2.0), product of:
            0.17551683 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.050121464 = queryNorm
            0.23214069 = fieldWeight in 690, 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=690)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

Date

23. 3.2013 13:22:36

0.004785695 = product of:
  0.01914278 = sum of:
    0.01914278 = weight(_text_:library in 2300) [ClassicSimilarity], result of:
      0.01914278 = score(doc=2300,freq=2.0), product of:
        0.1317883 = queryWeight, product of:
          2.6293786 = idf(docFreq=8668, maxDocs=44218)
          0.050121464 = queryNorm
        0.14525402 = fieldWeight in 2300, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          2.6293786 = idf(docFreq=8668, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2300)
  0.25 = coord(1/4)

0.0042442293 = product of:
  0.016976917 = sum of:
    0.016976917 = product of:
      0.033953834 = sum of:
        0.033953834 = weight(_text_:22 in 1107) [ClassicSimilarity], result of:
          0.033953834 = score(doc=1107,freq=2.0), product of:
            0.17551683 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.050121464 = queryNorm
            0.19345059 = fieldWeight in 1107, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1107)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

Date

28.10.2013 19:22:57