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  • × author_ss:"Liu, J."
  1. Liu, J.: Review and prospect for centralized cataloging in China (1999) 0.02 
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    Abstract
    With a long history, cataloging has developed gradually in China. Nowadays, both the content and method of cataloging have changed in many ways. As a type of organizing cataloging model, centralized cataloging came into being in 1936 in China. The history and the current status of centralized cataloging in China are described in the paper. The prospect for cataloging in the country is also discussed. In respect to resource building and sharing, cooperative cataloging is thought to be the best way to develop cataloging in the future.
  2. Liu, J.: CIP in China : the development and status quo (1996) 0.01 
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    Abstract
    This paper provides a brief overview of the development and current status of the Cataloging-in-Publication (CIP) project in China. The China CIP project is a new one implemented in 1993. In the paper, the development of CIP in the world is described, followed by when and how it was introduced into China. The paper tells the significances of CIP in detail. The implementation of the CIP project and differences of CIP work in China from that in the United States are also reflected here. Finally, the contribution discusses the problems in implementing the project and suggests ways to solve them. The project combines the publishing house, library, and distributor into the document information system. CIP is not only a kind of cataloging, but also a bond among them. It is believed that the CIP project in China has a bright future.
    Source
    Cataloging and classification quarterly. 22(1996) no.1, S.69-76
  3. Liu, J.; Belkin, N.J.: Personalizing information retrieval for multi-session tasks : examining the roles of task stage, task type, and topic knowledge on the interpretation of dwell time as an indicator of document usefulness (2015) 0.01 
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    Abstract
    Personalization of information retrieval tailors search towards individual users to meet their particular information needs by taking into account information about users and their contexts, often through implicit sources of evidence such as user behaviors. This study looks at users' dwelling behavior on documents and several contextual factors: the stage of users' work tasks, task type, and users' knowledge of task topics, to explore whether or not taking account contextual factors could help infer document usefulness from dwell time. A controlled laboratory experiment was conducted with 24 participants, each coming 3 times to work on 3 subtasks in a general work task. The results show that task stage could help interpret certain types of dwell time as reliable indicators of document usefulness in certain task types, as was topic knowledge, and the latter played a more significant role when both were available. This study contributes to a better understanding of how dwell time can be used as implicit evidence of document usefulness, as well as how contextual factors can help interpret dwell time as an indicator of usefulness. These findings have both theoretical and practical implications for using behaviors and contextual factors in the development of personalization systems.
  4. Liu, J.; Wu, Y.; Zhou, L.: ¬A hybrid method for abstracting newspaper articles (1999) 0.01 
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    Abstract
    This paper introduces a hybrid method for abstracting Chinese text. It integrates the statistical approach with language understanding. Some linguistics heuristics and segmentation are also incorporated into the abstracting process. The prototype system is of a multipurpose type catering for various users with different reqirements. Initial responses show that the proposed method contributes much to the flexibility and accuracy of the automatic Chinese abstracting system. In practice, the present work provides a path to developing an intelligent Chinese system for automating the information
  5. Jiang, X.; Liu, J.: Extracting the evolutionary backbone of scientific domains : the semantic main path network analysis approach based on citation context analysis (2023) 0.00 
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    Abstract
    Main path analysis is a popular method for extracting the scientific backbone from the citation network of a research domain. Existing approaches ignored the semantic relationships between the citing and cited publications, resulting in several adverse issues, in terms of coherence of main paths and coverage of significant studies. This paper advocated the semantic main path network analysis approach to alleviate these issues based on citation function analysis. A wide variety of SciBERT-based deep learning models were designed for identifying citation functions. Semantic citation networks were built by either including important citations, for example, extension, motivation, usage and similarity, or excluding incidental citations like background and future work. Semantic main path network was built by merging the top-K main paths extracted from various time slices of semantic citation network. In addition, a three-way framework was proposed for the quantitative evaluation of main path analysis results. Both qualitative and quantitative analysis on three research areas of computational linguistics demonstrated that, compared to semantics-agnostic counterparts, different types of semantic main path networks provide complementary views of scientific knowledge flows. Combining them together, we obtained a more precise and comprehensive picture of domain evolution and uncover more coherent development pathways between scientific ideas.
  6. Zhou, D.; Lawless, S.; Wu, X.; Zhao, W.; Liu, J.: ¬A study of user profile representation for personalized cross-language information retrieval (2016) 0.00 
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    Date
    20. 1.2015 18:30:22
  7. Zhang, Y.; Liu, J.; Song, S.: ¬The design and evaluation of a nudge-based interface to facilitate consumers' evaluation of online health information credibility (2023) 0.00 
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    Date
    22. 6.2023 18:18:34
  8. Jiang, Y.; Meng, R.; Huang, Y.; Lu, W.; Liu, J.: Generating keyphrases for readers : a controllable keyphrase generation framework (2023) 0.00 
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    Date
    22. 6.2023 14:55:20

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