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  • × year_i:[2010 TO 2020}
  • × author_ss:"Wang, P."
  1. Wang, P.: Information behavior and seeking (2011) 0.01 
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    Source
    Interactive information seeking, behaviour and retrieval. Eds.: Ruthven, I. u. D. Kelly
    Theme
    Information
  2. Cheng, A.-S.; Fleischmann, K.R.; Wang, P.; Ishita, E.; Oard, D.W.: ¬The role of innovation and wealth in the net neutrality debate : a content analysis of human values in congressional and FCC hearings (2012) 0.00 
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    Abstract
    Net neutrality is the focus of an important policy debate that is tied to technological innovation, economic development, and information access. We examine the role of human values in shaping the Net neutrality debate through a content analysis of testimonies from U.S. Senate and FCC hearings on Net neutrality. The analysis is based on a coding scheme that we developed based on a pilot study in which we used the Schwartz Value Inventory. We find that the policy debate surrounding Net neutrality revolves primarily around differences in the frequency of expression of the values of innovation and wealth, such that the proponents of Net neutrality more frequently invoke innovation, while the opponents of Net neutrality more frequently invoke wealth in their prepared testimonies. The paper provides a novel approach for examining the Net neutrality debate and sheds light on the connection between information policy and research on human values.
    Source
    Journal of the American Society for Information Science and Technology. 63(2012) no.7, S.1360-1373
  3. Wang, P.; Hao, T.; Yan, J.; Jin, L.: Large-scale extraction of drug-disease pairs from the medical literature (2017) 0.00 
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    Abstract
    Automatic extraction of large-scale and accurate drug-disease pairs from the medical literature plays an important role for drug repurposing. However, many existing extraction methods are mainly in a supervised manner. It is costly and time-consuming to manually label drug-disease pairs datasets. There are many drug-disease pairs buried in free text. In this work, we first leverage a pattern-based method to automatically extract drug-disease pairs with treatment and inducement relationships from free text. Then, to reflect a drug-disease relation, a network embedding algorithm is proposed to calculate the degree of correlation of a drug-disease pair. In the experiments, we use the method to extract treatment and inducement drug-disease pairs from 27 million medical abstracts and titles available on PubMed. We extract 138,318 unique treatment pairs and 75,396 unique inducement pairs. Our algorithm achieves a precision of 0.912 and a recall of 0.898 in extracting the frequent treatment drug-disease pairs, and a precision of 0.923 and a recall of 0.833 in extracting the frequent inducement drug-disease pairs. Besides, our proposed information network embedding algorithm can efficiently reflect the degree of correlation of drug-disease pairs. Our algorithm can achieve a precision of 0.802, a recall of 0.783 in the fine-grained evaluation of extracting frequent pairs.
    Footnote
    Beitrag in einem Special issue on biomedical information retrieval.
    Source
    Journal of the Association for Information Science and Technology. 68(2017) no.11, S.2649-2661

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