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  • × language_ss:"e"
  • × theme_ss:"Data Mining"
  1. Chen, S.Y.; Liu, X.: ¬The contribution of data mining to information science : making sense of it all (2005) 0.03
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    Source
    Journal of information science. 30(2005) no.6, S.550-
    Year
    2005
  2. Liu, W.; Weichselbraun, A.; Scharl, A.; Chang, E.: Semi-automatic ontology extension using spreading activation (2005) 0.02
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    Source
    Journal of universal knowledge management. 0(2005) no.1, S.50-58
    Year
    2005
  3. Chowdhury, G.G.: Template mining for information extraction from digital documents (1999) 0.02
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    Date
    2. 4.2000 18:01:22
  4. Wu, T.; Pottenger, W.M.: ¬A semi-supervised active learning algorithm for information extraction from textual data (2005) 0.01
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    Source
    Journal of the American Society for Information Science and Technology. 56(2005) no.3, S.258-271
    Year
    2005
  5. Lihui, C.; Lian, C.W.: Using Web structure and summarisation techniques for Web content mining (2005) 0.01
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    Source
    Information processing and management. 41(2005) no.5, S.1225-1242
    Year
    2005
  6. KDD : techniques and applications (1998) 0.01
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    Footnote
    A special issue of selected papers from the Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'97), held Singapore, 22-23 Feb 1997
  7. Matson, L.D.; Bonski, D.J.: Do digital libraries need librarians? (1997) 0.01
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    Date
    22.11.1998 18:57:22
  8. Amir, A.; Feldman, R.; Kashi, R.: ¬A new and versatile method for association generation (1997) 0.01
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    Source
    Information systems. 22(1997) nos.5/6, S.333-347
  9. Suakkaphong, N.; Zhang, Z.; Chen, H.: Disease named entity recognition using semisupervised learning and conditional random fields (2011) 0.01
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    Abstract
    Information extraction is an important text-mining task that aims at extracting prespecified types of information from large text collections and making them available in structured representations such as databases. In the biomedical domain, information extraction can be applied to help biologists make the most use of their digital-literature archives. Currently, there are large amounts of biomedical literature that contain rich information about biomedical substances. Extracting such knowledge requires a good named entity recognition technique. In this article, we combine conditional random fields (CRFs), a state-of-the-art sequence-labeling algorithm, with two semisupervised learning techniques, bootstrapping and feature sampling, to recognize disease names from biomedical literature. Two data-processing strategies for each technique also were analyzed: one sequentially processing unlabeled data partitions and another one processing unlabeled data partitions in a round-robin fashion. The experimental results showed the advantage of semisupervised learning techniques given limited labeled training data. Specifically, CRFs with bootstrapping implemented in sequential fashion outperformed strictly supervised CRFs for disease name recognition. The project was supported by NIH/NLM Grant R33 LM07299-01, 2002-2005.
  10. Hofstede, A.H.M. ter; Proper, H.A.; Van der Weide, T.P.: Exploiting fact verbalisation in conceptual information modelling (1997) 0.01
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    Source
    Information systems. 22(1997) nos.5/6, S.349-385
  11. Hallonsten, O.; Holmberg, D.: Analyzing structural stratification in the Swedish higher education system : data contextualization with policy-history analysis (2013) 0.01
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    Date
    22. 3.2013 19:43:01
  12. Vaughan, L.; Chen, Y.: Data mining from web search queries : a comparison of Google trends and Baidu index (2015) 0.01
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    Source
    Journal of the Association for Information Science and Technology. 66(2015) no.1, S.13-22
  13. Fonseca, F.; Marcinkowski, M.; Davis, C.: Cyber-human systems of thought and understanding (2019) 0.01
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    Date
    7. 3.2019 16:32:22
  14. Information visualization in data mining and knowledge discovery (2002) 0.00
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    Date
    23. 3.2008 19:10:22