Search (13 results, page 1 of 1)

  • × year_i:[2010 TO 2020}
  • × theme_ss:"Computerlinguistik"
  1. Sprachtechnologie : ein Überblick (2012) 0.04 
    0.035238802 = product of:
  0.070477605 = sum of:
    0.070477605 = product of:
      0.14095521 = sum of:
        0.14095521 = weight(_text_:z in 1750) [ClassicSimilarity], result of:
          0.14095521 = score(doc=1750,freq=8.0), product of:
            0.23903055 = queryWeight, product of:
              5.337313 = idf(docFreq=577, maxDocs=44218)
              0.04478481 = queryNorm
            0.58969533 = fieldWeight in 1750, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              5.337313 = idf(docFreq=577, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1750)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
    Abstract
    Seit mehr als einem halben Jahrhundert existieren ernsthafte und ernst zu nehmende Versuche, menschliche Sprache maschinell zu verarbeiten. Maschinelle Übersetzung oder "natürliche" Dialoge mit Computern gehören zu den ersten Ideen, die den Bereich der späteren Computerlinguistik oder Sprachtechnologie abgesteckt und deren Vorhaben geleitet haben. Heute ist dieser auch maschinelle Sprachverarbeitung (natural language processing, NLP) genannte Bereich stark ausdiversifiziert: Durch die rapide Entwicklung der Informatik ist vieles vorher Unvorstellbare Realität (z. B. automatische Telefonauskunft), einiges früher Unmögliche immerhin möglich geworden (z. B. Handhelds mit Sprachein- und -ausgabe als digitale persönliche (Informations-)Assistenten). Es gibt verschiedene Anwendungen der Computerlinguistik, von denen einige den Sprung in die kommerzielle Nutzung geschafft haben (z. B. Diktiersysteme, Textklassifikation, maschinelle Übersetzung). Immer noch wird an natürlichsprachlichen Systemen (natural language systems, NLS) verschiedenster Funktionalität (z. B. zur Beantwortung beliebiger Fragen oder zur Generierung komplexer Texte) intensiv geforscht, auch wenn die hoch gesteckten Ziele von einst längst nicht erreicht sind (und deshalb entsprechend "heruntergefahren" wurden). Wo die maschinelle Sprachverarbeitung heute steht, ist allerdings angesichts der vielfältigen Aktivitäten in der Computerlinguistik und Sprachtechnologie weder offensichtlich noch leicht in Erfahrung zu bringen (für Studierende des Fachs und erst recht für Laien). Ein Ziel dieses Buches ist, es, die aktuelle Literaturlage in dieser Hinsicht zu verbessern, indem spezifisch systembezogene Aspekte der Computerlinguistik als Überblick über die Sprachtechnologie zusammengetragen werden.
  2. Savoy, J.: Text representation strategies : an example with the State of the union addresses (2016) 0.02 
    0.024917595 = product of:
  0.04983519 = sum of:
    0.04983519 = product of:
      0.09967038 = sum of:
        0.09967038 = weight(_text_:z in 3042) [ClassicSimilarity], result of:
          0.09967038 = score(doc=3042,freq=4.0), product of:
            0.23903055 = queryWeight, product of:
              5.337313 = idf(docFreq=577, maxDocs=44218)
              0.04478481 = queryNorm
            0.41697758 = fieldWeight in 3042, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              5.337313 = idf(docFreq=577, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3042)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
    Abstract
    Based on State of the Union addresses from 1790 to 2014 (225 speeches delivered by 42 presidents), this paper describes and evaluates different text representation strategies. To determine the most important words of a given text, the term frequencies (tf) or the tf?idf weighting scheme can be applied. Recently, latent Dirichlet allocation (LDA) has been proposed to define the topics included in a corpus. As another strategy, this study proposes to apply a vocabulary specificity measure (Z?score) to determine the most significantly overused word-types or short sequences of them. Our experiments show that the simple term frequency measure is not able to discriminate between specific terms associated with a document or a set of texts. Using the tf idf or LDA approach, the selection requires some arbitrary decisions. Based on the term-specific measure (Z?score), the term selection has a clear theoretical basis. Moreover, the most significant sentences for each presidency can be determined. As another facet, we can visualize the dynamic evolution of usage of some terms associated with their specificity measures. Finally, this technique can be employed to define the most important lexical leaders introducing terms overused by the k following presidencies.
  3. Endres-Niggemeyer, B.: Thinkie: Lautes Denken mit Spracherkennung (mobil) (2013) 0.02 
    0.021143282 = product of:
  0.042286564 = sum of:
    0.042286564 = product of:
      0.08457313 = sum of:
        0.08457313 = weight(_text_:z in 1145) [ClassicSimilarity], result of:
          0.08457313 = score(doc=1145,freq=2.0), product of:
            0.23903055 = queryWeight, product of:
              5.337313 = idf(docFreq=577, maxDocs=44218)
              0.04478481 = queryNorm
            0.35381722 = fieldWeight in 1145, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.337313 = idf(docFreq=577, maxDocs=44218)
              0.046875 = fieldNorm(doc=1145)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
    Abstract
    Lautes Denken ist eine bewährte Methode zum Erforschen kognitiver Prozesse. Es wird in vielen Disziplinen benutzt, z. B. um aufzudecken, welche Erfahrungen Benutzer bei der Interaktion mit Computerschnittstellen machen. Nach einer kurzen Erklärung des Lauten Denkens wird die App Thinkie vorgestellt. Thinkie ist eine mobile Lösung für das Laute Denken auf iPhone und iPad. Die Testperson nimmt auf dem iPhone den Ton auf. Die Spracherkennungssoftware Siri (http://www.apple.com/de/ios/siri/) transkribiert ihn. Parallel wird auf dem iPad oder einem anderen Gerät gefilmt. Auf dem iPad kann man - mit Video im Blick - das Transkript aufarbeiten und interpretieren. Die Textdateien transportiert Thinkie über eine Cloud-Kollektion, die Filme werden mit iTunes übertragen. Thinkie ist noch nicht tauglich für den praktischen Gebrauch. Noch sind die Sequenzen zu kurz, die Siri verarbeiten kann. Das wird sich ändern.
  4. AL-Smadi, M.; Jaradat, Z.; AL-Ayyoub, M.; Jararweh, Y.: Paraphrase identification and semantic text similarity analysis in Arabic news tweets using lexical, syntactic, and semantic features (2017) 0.02 
    0.021143282 = product of:
  0.042286564 = sum of:
    0.042286564 = product of:
      0.08457313 = sum of:
        0.08457313 = weight(_text_:z in 5095) [ClassicSimilarity], result of:
          0.08457313 = score(doc=5095,freq=2.0), product of:
            0.23903055 = queryWeight, product of:
              5.337313 = idf(docFreq=577, maxDocs=44218)
              0.04478481 = queryNorm
            0.35381722 = fieldWeight in 5095, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.337313 = idf(docFreq=577, maxDocs=44218)
              0.046875 = fieldNorm(doc=5095)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
  5. Ye, Z.; He, B.; Wang, L.; Luo, T.: Utilizing term proximity for blog post retrieval (2013) 0.02 
    0.017619401 = product of:
  0.035238802 = sum of:
    0.035238802 = product of:
      0.070477605 = sum of:
        0.070477605 = weight(_text_:z in 1126) [ClassicSimilarity], result of:
          0.070477605 = score(doc=1126,freq=2.0), product of:
            0.23903055 = queryWeight, product of:
              5.337313 = idf(docFreq=577, maxDocs=44218)
              0.04478481 = queryNorm
            0.29484767 = fieldWeight in 1126, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.337313 = idf(docFreq=577, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1126)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
  6. Luo, Z.; Yu, Y.; Osborne, M.; Wang, T.: Structuring tweets for improving Twitter search (2015) 0.02 
    0.017619401 = product of:
  0.035238802 = sum of:
    0.035238802 = product of:
      0.070477605 = sum of:
        0.070477605 = weight(_text_:z in 2335) [ClassicSimilarity], result of:
          0.070477605 = score(doc=2335,freq=2.0), product of:
            0.23903055 = queryWeight, product of:
              5.337313 = idf(docFreq=577, maxDocs=44218)
              0.04478481 = queryNorm
            0.29484767 = fieldWeight in 2335, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.337313 = idf(docFreq=577, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2335)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
  7. Lian, T.; Yu, C.; Wang, W.; Yuan, Q.; Hou, Z.: Doctoral dissertations on tourism in China : a co-word analysis (2016) 0.02 
    0.017619401 = product of:
  0.035238802 = sum of:
    0.035238802 = product of:
      0.070477605 = sum of:
        0.070477605 = weight(_text_:z in 3178) [ClassicSimilarity], result of:
          0.070477605 = score(doc=3178,freq=2.0), product of:
            0.23903055 = queryWeight, product of:
              5.337313 = idf(docFreq=577, maxDocs=44218)
              0.04478481 = queryNorm
            0.29484767 = fieldWeight in 3178, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.337313 = idf(docFreq=577, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3178)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
  8. Lezius, W.: Morphy - Morphologie und Tagging für das Deutsche (2013) 0.01 
    0.012135447 = product of:
  0.024270894 = sum of:
    0.024270894 = product of:
      0.048541788 = sum of:
        0.048541788 = weight(_text_:22 in 1490) [ClassicSimilarity], result of:
          0.048541788 = score(doc=1490,freq=2.0), product of:
            0.15682878 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04478481 = queryNorm
            0.30952093 = fieldWeight in 1490, 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=1490)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
    Date
    22. 3.2015 9:30:24
  9. Huo, W.: Automatic multi-word term extraction and its application to Web-page summarization (2012) 0.01 
    0.0091015855 = product of:
  0.018203171 = sum of:
    0.018203171 = product of:
      0.036406342 = sum of:
        0.036406342 = weight(_text_:22 in 563) [ClassicSimilarity], result of:
          0.036406342 = score(doc=563,freq=2.0), product of:
            0.15682878 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04478481 = queryNorm
            0.23214069 = fieldWeight in 563, 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=563)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
    Date
    10. 1.2013 19:22:47
  10. Lawrie, D.; Mayfield, J.; McNamee, P.; Oard, P.W.: Cross-language person-entity linking from 20 languages (2015) 0.01 
    0.0091015855 = product of:
  0.018203171 = sum of:
    0.018203171 = product of:
      0.036406342 = sum of:
        0.036406342 = weight(_text_:22 in 1848) [ClassicSimilarity], result of:
          0.036406342 = score(doc=1848,freq=2.0), product of:
            0.15682878 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04478481 = queryNorm
            0.23214069 = fieldWeight in 1848, 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=1848)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
    Abstract
    The goal of entity linking is to associate references to an entity that is found in unstructured natural language content to an authoritative inventory of known entities. This article describes the construction of 6 test collections for cross-language person-entity linking that together span 22 languages. Fully automated components were used together with 2 crowdsourced validation stages to affordably generate ground-truth annotations with an accuracy comparable to that of a completely manual process. The resulting test collections each contain between 642 (Arabic) and 2,361 (Romanian) person references in non-English texts for which the correct resolution in English Wikipedia is known, plus a similar number of references for which no correct resolution into English Wikipedia is believed to exist. Fully automated cross-language person-name linking experiments with 20 non-English languages yielded a resolution accuracy of between 0.84 (Serbian) and 0.98 (Romanian), which compares favorably with previously reported cross-language entity linking results for Spanish.
  11. Fóris, A.: Network theory and terminology (2013) 0.01 
    0.0075846547 = product of:
  0.015169309 = sum of:
    0.015169309 = product of:
      0.030338619 = sum of:
        0.030338619 = weight(_text_:22 in 1365) [ClassicSimilarity], result of:
          0.030338619 = score(doc=1365,freq=2.0), product of:
            0.15682878 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04478481 = queryNorm
            0.19345059 = fieldWeight in 1365, 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=1365)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
    Date
    2. 9.2014 21:22:48
  12. Rötzer, F.: KI-Programm besser als Menschen im Verständnis natürlicher Sprache (2018) 0.01 
    0.0060677235 = product of:
  0.012135447 = sum of:
    0.012135447 = product of:
      0.024270894 = sum of:
        0.024270894 = weight(_text_:22 in 4217) [ClassicSimilarity], result of:
          0.024270894 = score(doc=4217,freq=2.0), product of:
            0.15682878 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04478481 = queryNorm
            0.15476047 = fieldWeight in 4217, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03125 = fieldNorm(doc=4217)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
    Date
    22. 1.2018 11:32:44
  13. Deventer, J.P. van; Kruger, C.J.; Johnson, R.D.: Delineating knowledge management through lexical analysis : a retrospective (2015) 0.01 
    0.005309258 = product of:
  0.010618516 = sum of:
    0.010618516 = product of:
      0.021237032 = sum of:
        0.021237032 = weight(_text_:22 in 3807) [ClassicSimilarity], result of:
          0.021237032 = score(doc=3807,freq=2.0), product of:
            0.15682878 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04478481 = queryNorm
            0.1354154 = fieldWeight in 3807, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.02734375 = fieldNorm(doc=3807)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
    Date
    20. 1.2015 18:30:22

Authors

Languages

Types

Themes