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  • × author_ss:"Chen, H."
  • × year_i:[2010 TO 2020}
  1. Chau, M.; Wong, C.H.; Zhou, Y.; Qin, J.; Chen, H.: Evaluating the use of search engine development tools in IT education (2010) 0.00 
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    Source
    Journal of the American Society for Information Science and Technology. 61(2010) no.2, S.288-299
    Year
    2010
  2. Huang, C.; Fu, T.; Chen, H.: Text-based video content classification for online video-sharing sites (2010) 0.00 
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    Source
    Journal of the American Society for Information Science and Technology. 61(2010) no.5, S.891-906
    Year
    2010
  3. Fu, T.; Abbasi, A.; Chen, H.: ¬A focused crawler for Dark Web forums (2010) 0.00 
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    Source
    Journal of the American Society for Information Science and Technology. 61(2010) no.6, S.1213-1231
    Year
    2010
  4. Hu, P.J.-H.; Hsu, F.-M.; Hu, H.-f.; Chen, H.: Agency satisfaction with electronic record management systems : a large-scale survey (2010) 0.00 
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    Source
    Journal of the American Society for Information Science and Technology. 61(2010) no.12, S.2559-2574
    Year
    2010
  5. Jiang, S.; Gao, Q.; Chen, H.; Roco, M.C.: ¬The roles of sharing, transfer, and public funding in nanotechnology knowledge-diffusion networks (2015) 0.00 
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    Abstract
    Understanding the knowledge-diffusion networks of patent inventors can help governments and businesses effectively use their investment to stimulate commercial science and technology development. Such inventor networks are usually large and complex. This study proposes a multidimensional network analysis framework that utilizes Exponential Random Graph Models (ERGMs) to simultaneously model knowledge-sharing and knowledge-transfer processes, examine their interactions, and evaluate the impacts of network structures and public funding on knowledge-diffusion networks. Experiments are conducted on a longitudinal data set that covers 2 decades (1991-2010) of nanotechnology-related US Patent and Trademark Office (USPTO) patents. The results show that knowledge sharing and knowledge transfer are closely interrelated. High degree centrality or boundary inventors play significant roles in the network, and National Science Foundation (NSF) public funding positively affects knowledge sharing despite its small fraction in overall funding and upstream research topics.
  6. Qu, B.; Cong, G.; Li, C.; Sun, A.; Chen, H.: ¬An evaluation of classification models for question topic categorization (2012) 0.00 
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    Abstract
    We study the problem of question topic classification using a very large real-world Community Question Answering (CQA) dataset from Yahoo! Answers. The dataset comprises 3.9 million questions and these questions are organized into more than 1,000 categories in a hierarchy. To the best knowledge, this is the first systematic evaluation of the performance of different classification methods on question topic classification as well as short texts. Specifically, we empirically evaluate the following in classifying questions into CQA categories: (a) the usefulness of n-gram features and bag-of-word features; (b) the performance of three standard classification algorithms (naive Bayes, maximum entropy, and support vector machines); (c) the performance of the state-of-the-art hierarchical classification algorithms; (d) the effect of training data size on performance; and (e) the effectiveness of the different components of CQA data, including subject, content, asker, and the best answer. The experimental results show what aspects are important for question topic classification in terms of both effectiveness and efficiency. We believe that the experimental findings from this study will be useful in real-world classification problems.

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