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- item-3 hasContent vanja-josifovski.
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- 743 creator chengxiang-zhai.
- 743 creator yue-lu.
- 743 type InProceedings.
- 743 label "Opinion Integration Through Semisupervised Topic Modeling".
- 743 sameAs 743.
- 743 abstract "Web 2.0 technology has enabled more and more people to freely express their opinions on the Web, making the Web an extremely valuable source for mining user opinions about all kinds of topics. In this paper we study how to automatically integrate opinions expressed in a well-written expert review with lots of opinions scattering in various sources such as blogspaces and forums. We formally define this new integration problem and propose to use semi-supervised topic models to solve the problem in a principled way. Experiments on integrating opinions about two quite different topics (a product and a political figure) show that the proposed method is effective for both topics and can generate useful aligned integrated opinion summaries. The proposed method is quite general. It can be used to integrate a well written review with opinions in an arbitrary text collection about any topic to potentially support many interesting applications in multiple domains.".
- 743 hasAuthorList authorList.
- 743 hasTopic World_Wide_Web.
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- 743 keyword "expert review".
- 743 keyword "opinion integration".
- 743 keyword "probabilistic topic modeling".
- 743 keyword "semi-supervised".
- 743 title "Opinion Integration Through Semisupervised Topic Modeling".
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- 749 creator elke-rundensteiner.
- 749 creator mingzhu-wei.
- 749 creator murali-mani.
- 749 type InProceedings.
- 749 label "Utility-driven Load Shedding for XML Stream Processing".
- 749 sameAs 749.
- 749 abstract "Because of the high volume and unpredictable arrival rate, stream processing systems may not always be able to keep up with the input data streams--- resulting in buffer overflow and uncontrolled loss of data. Load shedding, the prevalent strategy for solving this overflow problem, has so far only been considered for relational stream processing, but not for XML. Shedding applied to XML stream processing brings new challenges due to complex nested nature of XML structures. In this paper, we tackle this unsolved XML shedding problem using a three-pronged approach. First, we develop an XQuery preference model that enables users to specify the relative importance of preserving different subpatterns in the XML result structure. This transforms shedding into the problem of rewriting the user query into shed queries that return approximate query answers with utility as measured by the given user preference model. Second, we develop a cost model to compare the performance of alternate shed queries. Third, we develop two shedding algorithms, OptShed and FastShed. OptShed guarantees to find an optimal solution however at the cost of exponential complexity. FastShed, as confirmed by our experiments, efficiently achieves a close-to-optimal result in a wide range of test cases. Finally we describe the in-automaton shedding mechanism for XQuery stream engines. The experimental results show that our proposed utility-driven shedding solutions consistently achieve higher utility results compared to the existing relational shedding techniques.".
- 749 hasAuthorList authorList.
- 749 hasTopic World_Wide_Web.
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- 749 keyword "Load shedding".
- 749 keyword "Preference model".
- 749 keyword "XML query processing".
- 749 keyword "XML stream".
- 749 title "Utility-driven Load Shedding for XML Stream Processing".
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- item-1 hasContent mingzhu-wei.
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- 765 creator gui-rong-xue.
- 765 creator qiang-yang.
- 765 creator wenyuan-dai.
- 765 creator xiao-ling.
- 765 creator yong-yu.
- 765 creator yun-jiang.
- 765 type InProceedings.
- 765 label "Can chinese web pages be classified with english data source?".
- 765 sameAs 765.
- 765 abstract "In the China Web Mining, classification often meets the situation of lacking labeled data, while there are plenty of labeled English Web pages available on the Web, e.g. the Open Directory Project. The information contained in the Chinese and English Web pages may differ somewhat, while their feature representations are different as well. In this paper, we propose a transfer learning method to solve this cross-language classification problem. The English labeled data are firstly translated to the low quality Chinese data, and then domain-transfer learning is applied to transferring only useful and accurate knowledge from translated English Web pages to the Chinese ones. Compare with previous semi-supervised learning methods, our method better considers the domain differences and transfers only useful knowledge and removes noises. The theoretical and empirical studies shows that our method is effective. Compared with several state-of-art algorithms, our approach shows significant improvements, while scaling very well.".
- 765 hasAuthorList authorList.
- 765 hasTopic World_Wide_Web.
- 765 isPartOf proceedings.
- 765 keyword "Cross-Language Classification".
- 765 keyword "Information Bottleneck".
- 765 title "Can chinese web pages be classified with english data source?".
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- item-1 hasContent xiao-ling.
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