Search (13 results, page 1 of 1)

  • × author_ss:"Chen, Z."
  1. Chen, Z.; Wenyin, L.; Zhang, F.; Li, M.; Zhang, H.: Web mining for Web image retrieval (2001) 0.02 
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    Abstract
    The popularity of digital images is rapidly increasing due to improving digital imaging technologies and convenient availability facilitated by the Internet. However, how to find user-intended images from the Internet is nontrivial. The main reason is that the Web images are usually not annotated using semantic descriptors. In this article, we present an effective approach to and a prototype system for image retrieval from the Internet using Web mining. The system can also serve as a Web image search engine. One of the key ideas in the approach is to extract the text information on the Web pages to semantically describe the images. The text description is then combined with other low-level image features in the image similarity assessment. Another main contribution of this work is that we apply data mining on the log of users' feedback to improve image retrieval performance in three aspects. First, the accuracy of the document space model of image representation obtained from the Web pages is improved by removing clutter and irrelevant text information. Second, to construct the user space model of users' representation of images, which is then combined with the document space model to eliminate mismatch between the page author's expression and the user's understanding and expectation. Third, to discover the relationship between low-level and high-level features, which is extremely useful for assigning the low-level features' weights in similarity assessment
    Source
    Journal of the American Society for Information Science and technology. 52(2001) no.10, S.831-839
  2. Chen, Z.; Huang, Y.; Tian, J.; Liu, X.; Fu, K.; Huang, T.: Joint model for subsentence-level sentiment analysis with Markov logic (2015) 0.02 
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    Abstract
    Sentiment analysis mainly focuses on the study of one's opinions that express positive or negative sentiments. With the explosive growth of web documents, sentiment analysis is becoming a hot topic in both academic research and system design. Fine-grained sentiment analysis is traditionally solved as a 2-step strategy, which results in cascade errors. Although joint models, such as joint sentiment/topic and maximum entropy (MaxEnt)/latent Dirichlet allocation, are proposed to tackle this problem of sentiment analysis, they focus on the joint learning of both aspects and sentiments. Thus, they are not appropriate to solve the cascade errors for sentiment analysis at the sentence or subsentence level. In this article, we present a novel jointly fine-grained sentiment analysis framework at the subsentence level with Markov logic. First, we divide the task into 2 separate stages (subjectivity classification and polarity classification). Then, the 2 separate stages are processed, respectively, with different feature sets, which are implemented by local formulas in Markov logic. Finally, global formulas in Markov logic are adopted to realize the interactions of the 2 separate stages. The joint inference of subjectivity and polarity helps prevent cascade errors. Experiments on a Chinese sentiment data set manifest that our joint model brings significant improvements.
    Source
    Journal of the Association for Information Science and Technology. 66(2015) no.9, S.1913-1922
  3. Chen, Z.: Knowledge discovery and system-user partnership : on a production 'adversarial partnership' approach (1994) 0.01 
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    Abstract
    Examines the relationship between systems and users from the knowledge discovery in databases or data mining perspecitives. A comprehensive study on knowledge discovery in human computer symbiosis is needed. Proposes a database-user adversarial partnership, which is general enough to cover knowledge discovery and security of issues related to databases and their users. It can be further generalized into system-user adversarial paertnership. Discusses opportunities provided by knowledge discovery techniques and potential social implications
  4. Chen, Z.: Enhancing database management to knowledge base management : the role of information retrieval technology (1994) 0.01 
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  5. Xu, Y.C..; Chen, Z.: Relevance judgment : what do information users consider beyond topicality? (2006) 0.01 
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    Source
    Journal of the American Society for Information Science and Technology. 57(2006) no.7, S.961-973
  6. Wenyin, L.; Chen, Z.; Li, M.; Zhang, H.: ¬A media agent for automatically builiding a personalized semantic index of Web media objects (2001) 0.01 
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    Source
    Journal of the American Society for Information Science and technology. 52(2001) no.10, S.853-855
  7. Lee, M.K.O.; Cheung, C.M.K.; Chen, Z.: Understanding user acceptance of multimedia messaging services : an empirical study (2007) 0.01 
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    Source
    Journal of the American Society for Information Science and Technology. 58(2007) no.13, S.2066-2077
  8. Ren, P.; Chen, Z.; Ma, J.; Zhang, Z.; Si, L.; Wang, S.: Detecting temporal patterns of user queries (2017) 0.01 
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    Source
    Journal of the Association for Information Science and Technology. 68(2017) no.1, S.113-128
  9. Yan, E.; Chen, Z.; Li, K.: Authors' status and the perceived quality of their work : measuring citation sentiment change in nobel articles (2020) 0.01 
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    Source
    Journal of the Association for Information Science and Technology. 71(2020) no.3, S.314-324
  10. Chen, Z.; Meng, X.; Fowler, R.H.; Zhu, B.: Real-time adaptive feature and document learning for Web search (2001) 0.00 
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    Source
    Journal of the American Society for Information Science and technology. 52(2001) no.8, S.655-665
  11. Chen, Z.; Fu, B.: On the complexity of Rocchio's similarity-based relevance feedback algorithm (2007) 0.00 
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    Source
    Journal of the American Society for Information Science and Technology. 58(2007) no.10, S.1392-1400
  12. Cui, C.; Ma, J.; Lian, T.; Chen, Z.; Wang, S.: Improving image annotation via ranking-oriented neighbor search and learning-based keyword propagation (2015) 0.00 
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    Source
    Journal of the Association for Information Science and Technology. 66(2015) no.1, S.82-98
  13. Lian, T.; Chen, Z.; Lin, Y.; Ma, J.: Temporal patterns of the online video viewing behavior of smart TV viewers (2018) 0.00 
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    Source
    Journal of the Association for Information Science and Technology. 69(2018) no.5, S.647-659

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