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Chau, M.; Wong, C.H.; Zhou, Y.; Qin, J.; Chen, H.: Evaluating the use of search engine development tools in IT education (2010)
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- Abstract
- It is important for education in computer science and information systems to keep up to date with the latest development in technology. With the rapid development of the Internet and the Web, many schools have included Internet-related technologies, such as Web search engines and e-commerce, as part of their curricula. Previous research has shown that it is effective to use search engine development tools to facilitate students' learning. However, the effectiveness of these tools in the classroom has not been evaluated. In this article, we review the design of three search engine development tools, SpidersRUs, Greenstone, and Alkaline, followed by an evaluation study that compared the three tools in the classroom. In the study, 33 students were divided into 13 groups and each group used the three tools to develop three independent search engines in a class project. Our evaluation results showed that SpidersRUs performed better than the two other tools in overall satisfaction and the level of knowledge gained in their learning experience when using the tools for a class project on Internet applications development.
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Qu, B.; Cong, G.; Li, C.; Sun, A.; Chen, H.: ¬An evaluation of classification models for question topic categorization (2012)
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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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Ku, Y.; Chiu, C.; Zhang, Y.; Chen, H.; Su, H.: Text mining self-disclosing health information for public health service (2014)
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Jiang, S.; Gao, Q.; Chen, H.; Roco, M.C.: ¬The roles of sharing, transfer, and public funding in nanotechnology knowledge-diffusion networks (2015)
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Chen, H.; Beaudoin, C.E.; Hong, H.: Teen online information disclosure : empirical testing of a protection motivation and social capital model (2016)
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Huang, C.; Fu, T.; Chen, H.: Text-based video content classification for online video-sharing sites (2010)
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Fu, T.; Abbasi, A.; Chen, H.: ¬A focused crawler for Dark Web forums (2010)
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Hu, P.J.-H.; Hsu, F.-M.; Hu, H.-f.; Chen, H.: Agency satisfaction with electronic record management systems : a large-scale survey (2010)
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Suakkaphong, N.; Zhang, Z.; Chen, H.: Disease named entity recognition using semisupervised learning and conditional random fields (2011)
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Liu, X.; Kaza, S.; Zhang, P.; Chen, H.: Determining inventor status and its effect on knowledge diffusion : a study on nanotechnology literature from China, Russia, and India (2011)
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Yang, M.; Kiang, M.; Chen, H.; Li, Y.: Artificial immune system for illicit content identification in social media (2012)
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Benjamin, V.; Chen, H.; Zimbra, D.: Bridging the virtual and real : the relationship between web content, linkage, and geographical proximity of social movements (2014)
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Chen, H.; Baptista Nunes, J.M.; Ragsdell, G.; An, X.: Somatic and cultural knowledge : drivers of a habitus-driven model of tacit knowledge acquisition (2019)
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