Search (49 results, page 2 of 3)

  • × theme_ss:"Data Mining"
  • × year_i:[2010 TO 2020}
  1. Berendt, B.; Krause, B.; Kolbe-Nusser, S.: Intelligent scientific authoring tools : interactive data mining for constructive uses of citation networks (2010) 0.00 
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  2. Berry, M.W.; Esau, R.; Kiefer, B.: ¬The use of text mining techniques in electronic discovery for legal matters (2012) 0.00 
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  3. Biskri, I.; Rompré, L.: Using association rules for query reformulation (2012) 0.00 
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  4. Chen, Y.-L.; Liu, Y.-H.; Ho, W.-L.: ¬A text mining approach to assist the general public in the retrieval of legal documents (2013) 0.00 
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  5. Sun, X.; Lin, H.: Topical community detection from mining user tagging behavior and interest (2013) 0.00 
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  6. Mohr, J.W.; Bogdanov, P.: Topic models : what they are and why they matter (2013) 0.00 
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  7. Qiu, X.Y.; Srinivasan, P.; Hu, Y.: Supervised learning models to predict firm performance with annual reports : an empirical study (2014) 0.00 
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  8. Frické, M.: Big data and its epistemology (2015) 0.00 
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  9. Song, J.; Huang, Y.; Qi, X.; Li, Y.; Li, F.; Fu, K.; Huang, T.: Discovering hierarchical topic evolution in time-stamped documents (2016) 0.00 
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  10. Carter, D.; Sholler, D.: Data science on the ground : hype, criticism, and everyday work (2016) 0.00 
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  11. Kraker, P.; Kittel, C,; Enkhbayar, A.: Open Knowledge Maps : creating a visual interface to the world's scientific knowledge based on natural language processing (2016) 0.00 
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  12. Maaten, L. van den; Hinton, G.: Visualizing non-metric similarities in multiple maps (2012) 0.00 
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  13. Wongthontham, P.; Abu-Salih, B.: Ontology-based approach for semantic data extraction from social big data : state-of-the-art and research directions (2018) 0.00 
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  14. Loonus, Y.: Einsatzbereiche der KI und ihre Relevanz für Information Professionals (2017) 0.00 
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    Abstract
    Es liegt in der Natur des Menschen, Erfahrungen und Ideen in Wort und Schrift mit anderen teilen zu wollen. So produzieren wir jeden Tag gigantische Mengen an Texten, die in digitaler Form geteilt und abgelegt werden. The Radicati Group schätzt, dass 2017 täglich 269 Milliarden E-Mails versendet und empfangen werden. Hinzu kommen größtenteils unstrukturierte Daten wie Soziale Medien, Presse, Websites und firmeninterne Systeme, beispielsweise in Form von CRM-Software oder PDF-Dokumenten. Der weltweite Bestand an unstrukturierten Daten wächst so rasant, dass es kaum möglich ist, seinen Umfang zu quantifizieren. Der Versuch, eine belastbare Zahl zu recherchieren, führt unweigerlich zu diversen Artikeln, die den Anteil unstrukturierter Texte am gesamten Datenbestand auf 80% schätzen. Auch wenn nicht mehr einwandfrei nachvollziehbar ist, woher diese Zahl stammt, kann bei kritischer Reflexion unseres Tagesablaufs kaum bezweifelt werden, dass diese Daten von großer wirtschaftlicher Relevanz sind.
  15. Suakkaphong, N.; Zhang, Z.; Chen, H.: Disease named entity recognition using semisupervised learning and conditional random fields (2011) 0.00 
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  16. Ma, Z.; Sun, A.; Cong, G.: On predicting the popularity of newly emerging hashtags in Twitter (2013) 0.00 
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  17. O'Brien, H.L.; Lebow, M.: Mixed-methods approach to measuring user experience in online news interactions (2013) 0.00 
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  18. Wei, C.-P.; Lee, Y.-H.; Chiang, Y.-S.; Chen, C.-T.; Yang, C.C.C.: Exploiting temporal characteristics of features for effectively discovering event episodes from news corpora (2014) 0.00 
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  19. Zhang, Z.; Li, Q.; Zeng, D.; Ga, H.: Extracting evolutionary communities in community question answering (2014) 0.00 
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  20. Ekbia, H.; Mattioli, M.; Kouper, I.; Arave, G.; Ghazinejad, A.; Bowman, T.; Suri, V.R.; Tsou, A.; Weingart, S.; Sugimoto, C.R.: Big data, bigger dilemmas : a critical review (2015) 0.00 
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