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  • × author_ss:"Li, T."
  1. Zhao, G.; Wu, J.; Wang, D.; Li, T.: Entity disambiguation to Wikipedia using collective ranking (2016) 0.00 
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    Abstract
    Entity disambiguation is a fundamental task of semantic Web annotation. Entity Linking (EL) is an essential procedure in entity disambiguation, which aims to link a mention appearing in a plain text to a structured or semi-structured knowledge base, such as Wikipedia. Existing research on EL usually annotates the mentions in a text one by one and treats entities independent to each other. However this might not be true in many application scenarios. For example, if two mentions appear in one text, they are likely to have certain intrinsic relationships. In this paper, we first propose a novel query expansion method for candidate generation utilizing the information of co-occurrences of mentions. We further propose a re-ranking model which can be iteratively adjusted based on the prediction in the previous round. Experiments on real-world data demonstrate the effectiveness of our proposed methods for entity disambiguation.
    Date
    24.10.2016 19:22:54
    Source
    Information processing and management. 52(2016) no.6, S.1247-1257
  2. Li, T.; Slee, T.: ¬The effects of information privacy concerns on digitizing personal health records (2014) 0.00 
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    Abstract
    Electronic health record (EHR) systems can improve service efficiency and quality within the health care sector and thus have been widely considered for adoption. Yet the introduction of such systems has caused much concern about patients' information privacy. This study provides new insights into how privacy concerns play a role in patients' decisions to permit digitization of their personal health information. We conducted an online experiment and collected data from 164 patients who are involved in the nonmandatory EHR adoption in the Netherlands. We found that the negative effect of information privacy concerns on patients' willingness to opt in is influenced by the degree of EHR system interoperability and patients' ability to control disclosure of their information. The results show that, for a networked EHR system, the negative effect of privacy concerns on opt-in behavior was reinforced more than for the stand-alone system. The results also suggest that giving patients greater ability to control their information can alleviate their privacy concerns when they make opt-in decisions. We discuss the implications of these findings.
    Source
    Journal of the Association for Information Science and Technology. 65(2014) no.8, S.1541-1554
  3. Li, T.; Zhu, S.; Ogihara, M.: Hierarchical document classification using automatically generated hierarchy (2007) 0.00 
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    Abstract
    Automated text categorization has witnessed a booming interest with the exponential growth of information and the ever-increasing needs for organizations. The underlying hierarchical structure identifies the relationships of dependence between different categories and provides valuable sources of information for categorization. Although considerable research has been conducted in the field of hierarchical document categorization, little has been done on automatic generation of topic hierarchies. In this paper, we propose the method of using linear discriminant projection to generate more meaningful intermediate levels of hierarchies in large flat sets of classes. The linear discriminant projection approach first transforms all documents onto a low-dimensional space and then clusters the categories into hier- archies accordingly. The paper also investigates the effect of using generated hierarchical structure for text classification. Our experiments show that generated hierarchies improve classification performance in most cases.
    Source
    Journal of intelligent information systems. 29(2007) no.2, S.211-230
  4. Li, T.; Zhu, S.; Ogihara, M.: Text categorization via generalized discriminant analysis (2008) 0.00 
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    Abstract
    Text categorization is an important research area and has been receiving much attention due to the growth of the on-line information and of Internet. Automated text categorization is generally cast as a multi-class classification problem. Much of previous work focused on binary document classification problems. Support vector machines (SVMs) excel in binary classification, but the elegant theory behind large-margin hyperplane cannot be easily extended to multi-class text classification. In addition, the training time and scaling are also important concerns. On the other hand, other techniques naturally extensible to handle multi-class classification are generally not as accurate as SVM. This paper presents a simple and efficient solution to multi-class text categorization. Classification problems are first formulated as optimization via discriminant analysis. Text categorization is then cast as the problem of finding coordinate transformations that reflects the inherent similarity from the data. While most of the previous approaches decompose a multi-class classification problem into multiple independent binary classification tasks, the proposed approach enables direct multi-class classification. By using generalized singular value decomposition (GSVD), a coordinate transformation that reflects the inherent class structure indicated by the generalized singular values is identified. Extensive experiments demonstrate the efficiency and effectiveness of the proposed approach.
    Source
    Information processing and management. 44(2008) no.5, S.1684-1697

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