Search (33 results, page 2 of 2)

  • × theme_ss:"Computerlinguistik"
  • × year_i:[2010 TO 2020}
  1. Ramisch, C.: Multiword expressions acquisition : a generic and open framework (2015) 0.00 
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    Classification
    BFP (FH K)
    GHBS
    BFP (FH K)
  2. Sankarasubramaniam, Y.; Ramanathan, K.; Ghosh, S.: Text summarization using Wikipedia (2014) 0.00 
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  3. Savoy, J.: Text representation strategies : an example with the State of the union addresses (2016) 0.00 
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    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.
  4. Lian, T.; Yu, C.; Wang, W.; Yuan, Q.; Hou, Z.: Doctoral dissertations on tourism in China : a co-word analysis (2016) 0.00 
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    Abstract
    The aim of this paper is to map the foci of research in doctoral dissertations on tourism in China. In the paper, coword analysis is applied, with keywords coming from six public dissertation databases, i.e. CDFD, Wanfang Data, NLC, CALIS, ISTIC, and NSTL, as well as some university libraries providing doctoral dissertations on tourism. Altogether we have examined 928 doctoral dissertations on tourism written between 1989 and 2013. Doctoral dissertations on tourism in China involve 36 first level disciplines and 102 secondary level disciplines. We collect the top 68 keywords of practical significance in tourism which are mentioned at least four times or more. These keywords are classified into 12 categories based on co-word analysis, including cluster analysis, strategic diagrams analysis, and social network analysis. According to the strategic diagram of the 12 categories, we find the mature and immature areas in tourism study. From social networks, we can see the social network maps of original co-occurrence matrix and k-cores analysis of binary matrix. The paper provides valuable insight into the study of tourism by analyzing doctoral dissertations on tourism in China.
  5. Lhadj, L.S.; Boughanem, M.; Amrouche, K.: Enhancing information retrieval through concept-based language modeling and semantic smoothing (2016) 0.00 
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  6. Järvelin, A.; Keskustalo, H.; Sormunen, E.; Saastamoinen, M.; Kettunen, K.: Information retrieval from historical newspaper collections in highly inflectional languages : a query expansion approach (2016) 0.00 
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  7. K., Vani; Gupta, D.: Unmasking text plagiarism using syntactic-semantic based natural language processing techniques : comparisons, analysis and challenges (2018) 0.00 
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  8. Gill, A.J.; Hinrichs-Krapels, S.; Blanke, T.; Grant, J.; Hedges, M.; Tanner, S.: Insight workflow : systematically combining human and computational methods to explore textual data (2017) 0.00 
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    Date
    16.11.2017 14:00:29
  9. Fóris, A.: Network theory and terminology (2013) 0.00 
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    Date
    2. 9.2014 21:22:48
  10. RWI/PH: Auf der Suche nach dem entscheidenden Wort : die Häufung bestimmter Wörter innerhalb eines Textes macht diese zu Schlüsselwörtern (2012) 0.00 
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    Content
    "Die Dresdner Wissenschaftler haben die semantischen Eigenschaften von Texten mathematisch untersucht, indem sie zehn verschiedene englische Texte in unterschiedlichen Formen kodierten. Dazu zählt unter anderem die englische Ausgabe von Leo Tolstois "Krieg und Frieden". Beispielsweise übersetzten die Forscher Buchstaben innerhalb eines Textes in eine Binär-Sequenz. Dazu ersetzten sie alle Vokale durch eine Eins und alle Konsonanten durch eine Null. Mit Hilfe weiterer mathematischer Funktionen beleuchteten die Wissenschaftler dabei verschiedene Ebenen des Textes, also sowohl einzelne Vokale, Buchstaben als auch ganze Wörter, die in verschiedenen Formen kodiert wurden. Innerhalb des ganzen Textes lassen sich so wiederkehrende Muster finden. Diesen Zusammenhang innerhalb des Textes bezeichnet man als Langzeitkorrelation. Diese gibt an, ob zwei Buchstaben an beliebig weit voneinander entfernten Textstellen miteinander in Verbindung stehen - beispielsweise gibt es wenn wir an einer Stelle einen Buchstaben "K" finden, eine messbare höhere Wahrscheinlichkeit den Buchstaben "K" einige Seiten später nochmal zu finden. "Es ist zu erwarten, dass wenn es in einem Buch an einer Stelle um Krieg geht, die Wahrscheinlichkeit hoch ist das Wort Krieg auch einige Seiten später zu finden. Überraschend ist es, dass wir die hohe Wahrscheinlichkeit auch auf der Buchstabenebene finden", so Altmann.
  11. Kajanan, S.; Bao, Y.; Datta, A.; VanderMeer, D.; Dutta, K.: Efficient automatic search query formulation using phrase-level analysis (2014) 0.00 
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  12. Rötzer, F.: KI-Programm besser als Menschen im Verständnis natürlicher Sprache (2018) 0.00 
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    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) 0.00 
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    Date
    20. 1.2015 18:30:22

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