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  • × theme_ss:"Computerlinguistik"
  1. Schmidt, R.: Maschinelle Text-Ton-Synchronisation in Wissenschaft und Wirtschaft (2000) 0.01
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    Source
    Sprachtechnologie für eine dynamische Wirtschaft im Medienzeitalter - Language technologies for dynamic business in the age of the media - L'ingénierie linguistique au service de la dynamisation économique à l'ère du multimédia: Tagungsakten der XXVI. Jahrestagung der Internationalen Vereinigung Sprache und Wirtschaft e.V., 23.-25.11.2000, Fachhochschule Köln. Hrsg.: K.-D. Schmitz
  2. Rapke, K.: Automatische Indexierung von Volltexten für die Gruner+Jahr Pressedatenbank (2001) 0.01
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  3. Li, W.; Wong, K.-F.; Yuan, C.: Toward automatic Chinese temporal information extraction (2001) 0.01
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  4. Manhart, K.: Digitales Kauderwelsch : Online-Übersetzungsdienste (2004) 0.01
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  5. Kishida, K.: Term disambiguation techniques based on target document collection for cross-language information retrieval : an empirical comparison of performance between techniques (2007) 0.01
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  6. Shaalan, K.; Raza, H.: NERA: Named Entity Recognition for Arabic (2009) 0.01
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  7. Sankarasubramaniam, Y.; Ramanathan, K.; Ghosh, S.: Text summarization using Wikipedia (2014) 0.01
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  8. Savoy, J.: Text representation strategies : an example with the State of the union addresses (2016) 0.01
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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.
  9. Lian, T.; Yu, C.; Wang, W.; Yuan, Q.; Hou, Z.: Doctoral dissertations on tourism in China : a co-word analysis (2016) 0.01
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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.
  10. Lhadj, L.S.; Boughanem, M.; Amrouche, K.: Enhancing information retrieval through concept-based language modeling and semantic smoothing (2016) 0.01
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  11. 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) 0.01
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  12. K., Vani; Gupta, D.: Unmasking text plagiarism using syntactic-semantic based natural language processing techniques : comparisons, analysis and challenges (2018) 0.01
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  13. Soni, S.; Lerman, K.; Eisenstein, J.: Follow the leader : documents on the leading edge of semantic change get more citations (2021) 0.01
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  14. Tao, J.; Zhou, L.; Hickey, K.: Making sense of the black-boxes : toward interpretable text classification using deep learning models (2023) 0.01
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  15. Laparra, E.; Binford-Walsh, A.; Emerson, K.; Miller, M.L.; López-Hoffman, L.; Currim, F.; Bethard, S.: Addressing structural hurdles for metadata extraction from environmental impact statements (2023) 0.01
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  16. Pinker, S.: Wörter und Regeln : Die Natur der Sprache (2000) 0.01
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    Date
    19. 7.2002 14:22:31
  17. Computational linguistics for the new millennium : divergence or synergy? Proceedings of the International Symposium held at the Ruprecht-Karls Universität Heidelberg, 21-22 July 2000. Festschrift in honour of Peter Hellwig on the occasion of his 60th birthday (2002) 0.01
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  18. Semantik, Lexikographie und Computeranwendungen : Workshop ... (Bonn) : 1995.01.27-28 (1996) 0.01
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    Date
    14. 4.2007 10:04:22
  19. Fóris, A.: Network theory and terminology (2013) 0.01
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    Date
    2. 9.2014 21:22:48
  20. Luo, L.; Ju, J.; Li, Y.-F.; Haffari, G.; Xiong, B.; Pan, S.: ChatRule: mining logical rules with large language models for knowledge graph reasoning (2023) 0.01
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    Date
    23.11.2023 19:07:22

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