Search (11 results, page 1 of 1)

  • × author_ss:"Liu, X."
  1. Zhang, X.; Fang, Y.; He, W.; Zhang, Y.; Liu, X.: Epistemic motivation, task reflexivity, and knowledge contribution behavior on team wikis : a cross-level moderation model (2019) 0.02
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  2. Zhang, C.; Liu, X.; Xu, Y.(C.); Wang, Y.: Quality-structure index : a new metric to measure scientific journal influence (2011) 0.01
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  3. Cui, Y.; Wang, Y.; Liu, X.; Wang, X.; Zhang, X.: Multidimensional scholarly citations : characterizing and understanding scholars' citation behaviors (2023) 0.01
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  4. Yang, Y.; Liu, X.: ¬A re-examination of text categorization methods (1999) 0.01
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  5. Liu, X.; Yu, S.; Janssens, F.; Glänzel, W.; Moreau, Y.; Moor, B.de: Weighted hybrid clustering by combining text mining and bibliometrics on a large-scale journal database (2010) 0.01
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  6. Liu, X.; Bu, Y.; Li, M.; Li, J.: Monodisciplinary collaboration disrupts science more than multidisciplinary collaboration (2024) 0.01
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  7. Liu, X.; Croft, W.B.: Statistical language modeling for information retrieval (2004) 0.01
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    Abstract
    This chapter reviews research and applications in statistical language modeling for information retrieval (IR), which has emerged within the past several years as a new probabilistic framework for describing information retrieval processes. Generally speaking, statistical language modeling, or more simply language modeling (LM), involves estimating a probability distribution that captures statistical regularities of natural language use. Applied to information retrieval, language modeling refers to the problem of estimating the likelihood that a query and a document could have been generated by the same language model, given the language model of the document either with or without a language model of the query. The roots of statistical language modeling date to the beginning of the twentieth century when Markov tried to model letter sequences in works of Russian literature (Manning & Schütze, 1999). Zipf (1929, 1932, 1949, 1965) studied the statistical properties of text and discovered that the frequency of works decays as a Power function of each works rank. However, it was Shannon's (1951) work that inspired later research in this area. In 1951, eager to explore the applications of his newly founded information theory to human language, Shannon used a prediction game involving n-grams to investigate the information content of English text. He evaluated n-gram models' performance by comparing their crossentropy an texts with the true entropy estimated using predictions made by human subjects. For many years, statistical language models have been used primarily for automatic speech recognition. Since 1980, when the first significant language model was proposed (Rosenfeld, 2000), statistical language modeling has become a fundamental component of speech recognition, machine translation, and spelling correction.
  8. Chen, Z.; Huang, Y.; Tian, J.; Liu, X.; Fu, K.; Huang, T.: Joint model for subsentence-level sentiment analysis with Markov logic (2015) 0.01
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  9. Jiang, Z.; Liu, X.; Chen, Y.: Recovering uncaptured citations in a scholarly network : a two-step citation analysis to estimate publication importance (2016) 0.01
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  10. Clewley, N.; Chen, S.Y.; Liu, X.: Cognitive styles and search engine preferences : field dependence/independence vs holism/serialism (2010) 0.01
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  11. Chen, M.; Liu, X.; Qin, J.: Semantic relation extraction from socially-generated tags : a methodology for metadata generation (2008) 0.00
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    Source
    Metadata for semantic and social applications : proceedings of the International Conference on Dublin Core and Metadata Applications, Berlin, 22 - 26 September 2008, DC 2008: Berlin, Germany / ed. by Jane Greenberg and Wolfgang Klas