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Domain adaptation for statistical machine translation and neural machine translation
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Zhang, Jian. - : Dublin City University. School of Computing, 2017. : Dublin City University. ADAPT, 2017
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In: Zhang, Jian orcid:0000-0001-5659-5865 (2017) Domain adaptation for statistical machine translation and neural machine translation. PhD thesis, Dublin City University. (2017)
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Fast gated neural domain adaptation: language model as a case study
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In: Zhang, Jian orcid:0000-0001-5659-5865 , Wu, Xiaofeng, Way, Andy orcid:0000-0001-5736-5930 and Liu, Qun orcid:0000-0002-7000-1792 (2017) Fast gated neural domain adaptation: language model as a case study. In: Proceedings of FETLT 2016: Future and Emerging Trends in Language Technologies, Machine Learning and Big Data, 30 Nov- 2 Dec 2016, Seville, Spain. (2017)
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Refining Image Categorization by Exploiting Web Images and General Corpus ...
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The global view of mRNA-related ceRNA cross-talks across cardiovascular diseases
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Two-Stage Friend Recommendation Based on Network Alignment and Series Expansion of Probabilistic Topic Model
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In: Faculty of Engineering and Information Sciences - Papers: Part B (2017)
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Abstract:
Precise friend recommendation is an important problem in social media. Although most social websites provide some kinds of auto friend searching functions, their accuracies are not satisfactory. In this paper, we propose a more precise auto friend recommendation method with two stages. In the first stage, by utilizing the information of the relationship between texts and users, as well as the friendship information between users, we align different social networks and choose some "possible friends." In the second stage, with the relationship between image features and users, we build a topic model to further refine the recommendation results. Because some traditional methods, such as variational inference and Gibbs sampling, have their limitations in dealing with our problem, we develop a novel method to find out the solution of the topic model based on series expansion. We conduct experiments on the Flickr dataset to show that the proposed algorithm recommends friends more precisely and faster than traditional methods.
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Keyword:
Engineering; Science and Technology Studies
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URL: https://ro.uow.edu.au/eispapers1/701
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