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检索条件"机构=State Key Lab of Intelligent Tech. and Systems"

共 105 条记录，以下是51-60 订阅

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Particle Mixed Membership Stochastic Block Model

Particle Mixed Membership Stochastic Block Model

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第8届语义知识与网络国际会议(2012 Eighth International Conference on Semanties,Knowledge and Grids )(SKG2012)

作者： Yan Zhang Qixia Jiang Maosong Sun State Key Laboratory on Intelligent Technology and Systems Tsinghua National Laboratory for Information Science and Technology Department of Computer Sci. and Tech. Tsinghua University Beijing 100084 China

Massive real-world data are network-structured,such as semantic web,social network,relationship between proteins,*** a network is an effective way for better understanding the properties of a network,while avoiding the complexity of the full *** paper proposes a novel hierarchical Bayesian model for relational data,which is an extension of Mixed Membership Stochastic Block *** previous work assumes edges to be of atomic,our model recognizes that each edge is composed of multiple elementary relationships and the weight on this edge is a includes all the weights of these elementary *** allows our model to incorporate more information about the network and increases its ability of uncertainty tolerance.A fast inference based on variational inference is *** results on a synthetic data and three real-world data sets demonstrate the effectiveness and the robustness of our method.

关键词： Stochastic processes Data models Communities Bayesian methods Uncertainty Correlation Convergence

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Left-to-Right Tree-to-String Decoding with Prediction 12

Left-to-Right Tree-to-String Decoding with Prediction

引用

Conference on Empirical Methods in Natural Language Processing

作者： Yang Feng Yang Liu Qun Liu Trevor Cohn Department of Computer Science The University of Sheffield Sheffield UK State Key Laboratory on Intelligent Technology and Systems Tsinghua National Laboratory for Information Science and Technology Department of Computer Sci. and Tech. Tsinghua University Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781622765034

Decoding algorithms for syntax based machine translation suffer from high computational complexity, a consequence of intersecting a language model with a context free grammar. Left-to-right decoding, which generates the target string in order, can improve decoding efficiency by simplifying the language model evaluation. This paper presents a novel left to right decoding algorithm for tree-to-string translation, using a bottom-up parsing strategy and dynamic future cost estimation for each partial translation. Our method outperforms previously published tree-to-string decoders, including a competing left-to-right method.

关键词： modelling languages DECODING decoding algorithm machine translation syntax context-free grammars

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Monte Carlo Methods for Maximum Margin Supervised Topic Models 12

Monte Carlo Methods for Maximum Margin Supervised Topic Mode...

引用

Annual Conference on Neural Information Processing systems

作者： Qixia Jiang Jun Zhu Maosong Sun Eric P. Xing Department of Computer Science & Technology Tsinghua National TNList LabState Key Lab of Intelligent Tech. & Sys. Tsinghua University Beijing 100084 China Department of Computer Science & Technology Tsinghua National TNList Lab School of Computer Science Carnegie Mellon University Pittsburgh PA 15213

ISBN: (纸本)9781627480031

An effective strategy to exploit the supervising side information for discovering predictive topic representations is to impose discriminative constraints induced by such information on the posterior distributions under a topic model. This strategy has been adopted by a number of supervised topic models, such as MedLDA, which employs max-margin posterior constraints. However, unlike the likelihood-based supervised topic models, of which posterior inference can be carried out using the Bayes' rule, the max-margin posterior constraints have made Monte Carlo methods infeasible or at least not directly applicable, thereby limited the choice of inference algorithms to be based on variational approximation with strict mean field assumptions. In this paper, we develop two efficient Monte Carlo methods under much weaker assumptions for max-margin supervised topic models based on an importance sampler and a collapsed Gibbs sampler, respectively, in a convex dual formulation. We report thorough experimental results that compare our approach favorably against existing alternatives in both accuracy and efficiency.

关键词： Monte Carlo tech.ique Inference algorithms Bayes samplers side information

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Visualizing timelines: Evolutionary summarization via iterative reinforcement between text and image streams 12

Visualizing timelines: Evolutionary summarization via iterat...

引用

21st ACM International Conference on Information and Knowledge Management, CIKM 2012

作者： Yan, Rui Wan, Xiaojun Lapata, Mirella Zhao, Wayne Xin Cheng, Pu-Jen Li, Xiaoming Dept. of Computer Science and Information Engineering National Taiwan University Taipei 10617 Taiwan Institute of Computer Science and Technology Peking University Beijing 100871 China Institute for Language Cognition and Computation University of Edinburgh Edinburgh EH8 9AB United Kingdom Dept. of Computer Science and Technology Peking University Beijing 10087 China State Key Lab. of Virtual Reality Tech. and Systems Beihang University Beijing 100083 China

ISBN: (纸本)9781450311564

We present a novel graph-based framework for timeline summarization, the task of creating different summaries for different timestamps but for the same topic. Our work extends timeline summarization to a multimodal setting and creates timelines that are both textual and visual. Our approach exploits the fact that news documents are often accompanied by pictures and the two share some common content. Our model optimizes local summary creation and global timeline generation jointly following an iterative approach based on mutual reinforcement and co-ranking. In our algorithm, individual summaries are generated by taking into account the mutual dependencies between sentences and images, and are iteratively refined by considering how they contribute to the global timeline and its coherence. Experiments on real-world datasets show that the timelines produced by our model outperform several competitive baselines both in terms of ROUGE and when assessed by human evaluators. © 2012 ACM.

关键词： Reinforcement

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Fast query recommendation by search 25

Fast query recommendation by search

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25th AAAI Conference on Artificial Intelligence and the 23rd Innovative Applications of Artificial Intelligence Conference, AAAI-11 / IAAI-11

作者： Jiang, Qixia Sun, Maosong State Key Laboratory on Intelligent Technology and Systems Department of Computer Sci. and Tech. Tsinghua University Beijing 100084 China

ISBN: (纸本)9781577355090

Query recommendation can not only effectively facilitate users to obtain their desired information but also increase ads' click-through rates. This paper presents a general and highly efficient method for query recommendation. Given query sessions, we automatically generate many similar and dissimilar query-pairs as the prior knowledge. Then we learn a transformation from the prior knowledge to move similar queries closer such that similar queries tend to have similar hash values. This is formulated as minimizing the empirical error on the prior knowledge while maximizing the gap between the data and some partition hyperplanes randomly generated in advance. In the recommendation stage, we search queries that have similar hash values to the given query, rank the found queries and return the top K queries as the recommendation result. All the experimental results demonstrate that our method achieves encouraging results in terms of efficiency and recommendation performance. Copyright © 2011, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： Artificial intelligence

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Clustering product features for opinion mining 11

Clustering product features for opinion mining

引用

Proceedings of the fourth ACM international conference on Web search and data mining

作者： Zhai, Zhongwu Liu, Bing Xu, Hua Jia, Peifa State Key Lab of Intelligent Tech. and Sys Dept. of Comp. Sci. and Tech. Tsinghua University China Dep. of Comp. Sci. University of Illinois Chicago United States

ISBN: (纸本)9781450304931

In sentiment analysis of product reviews, one important problem is to produce a summary of opinions based on product features/attributes (also called aspects). However, for the same feature, people can express it with many different words or phrases. To produce a useful summary, these words and phrases, which are domain synonyms, need to be grouped under the same feature group. Although several methods have been proposed to extract product features from reviews, limited work has been done on clustering or grouping of synonym features. This paper focuses on this task. Classic methods for solving this problem are based on unsupervised learning using some forms of distributional similarity. However, we found that these methods do not do well. We then model it as a semi-supervised learning problem. Lexical characteristics of the problem are exploited to automatically identify some labeled examples. Empirical evaluation shows that the proposed method outperforms existing state-of-the-art methods by a large margin. Copyright 2011 ACM.

关键词： Sentiment analysis

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Constrained LDA for grouping product features in opinion mining

Constrained LDA for grouping product features in opinion min...

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作者： Zhai, Zhongwu Liu, Bing Xu, Hua Jia, Peifa State Key Lab. of Intelligent Tech. and Sys. Dept. of Comp. Sci. and Tech. Tsinghua Univ. China Dept. of Comp. Sci. University of Illinois Chicago IL United States

ISBN: (纸本)9783642208409

In opinion mining of product reviews, one often wants to produce a summary of opinions based on product features. However, for the same feature, people can express it with different words and phrases. To produce an effective summary, these words and phrases, which are domain synonyms, need to be grouped under the same feature. Topic modeling is a suitable method for the task. However, instead of simply letting topic modeling find groupings freely, we believe it is possible to do better by giving it some pre-existing knowledge in the form of automatically extracted constraints. In this paper, we first extend a popular topic modeling method, called Latent Dirichlet Allocation (LDA), with the ability to process large scale constraints. Then, two novel methods are proposed to extract two types of constraints automatically. Finally, the resulting constrained-LDA and the extracted constraints are applied to group product features. Experiments show that constrained-LDA outperforms the original LDA and the latest mLSA by a large margin. © 2011 Springer-Verlag.

关键词： Sentiment analysis

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On bias problem in relevance feedback 11

On bias problem in relevance feedback

引用

20th ACM Conference on Information and Knowledge Management, CIKM'11

作者： Xing, Qianli Zhang, Yi Zhang, Lanbo State Key Lab. of Intelligent Tech. and Sys. Dept. of Computer Science and Technology Tsinghua University Beijing 100084 China School of Engineering University of California Santa Cruz Santa Cruz CA 95064 United States

ISBN: (纸本)9781450307178

Relevance feedback is an effective approach to improve retrieval quality over the initial query. Typical relevance feedback methods usually select top-ranked documents for relevance judgments, then query expansion or model updating are carried out based on the feedback documents. However, the number of feedback documents is usually limited due to expensive human labeling. Thus relevant documents in the feedback set are hardly representative of all relevant documents and the feedback set is actually biased. As a result, the performance of relevance feedback will get hurt. In this paper, we first show how and where the bias problem exists through experiments. Then we study how the bias can be reduced by utilizing the unlabeled documents. After analyzing the usefulness of a document to relevance feedback, we propose an approach that extends the feedback set with carefully selected unlabeled documents by heuristics. Our experiment results show that the extended feedback set has less bias than the original feedback set and better performance can be achieved when the extended feedback set is used for relevance feedback. © 2011 ACM.

关键词： novelty heuristic relevance feedback bias

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Surface enhanced raman scattering in vertical nano optical antennas integrated with spiral ring gratings

Surface enhanced raman scattering in vertical nano optical a...

引用

Frontiers in Optics, FiO 2011

作者： Yang, Tian Liu, Baoan Shi, Chuan Univ. of Michigan-Shanghai National Key Lab. of Nano/Micro Fabrication Tech. Jiao Tong Univ. Joint Inst. Key Lab. for Thin Film and Microfabrication of the Ministry of Edu. State Key Lab of Advanced Optical Communication Systems and Networks Shanghai Jiao Tong Univ Shanghai 200240 China

We propose top-down plasmonic devices which are capable of single molecule SERS detection. The devices consist of vertical nano optical antennas integrated with spiral ring gratings. Electromagnetic SERS factors over ... 详细信息

ISBN: (纸本)9781557529176

关键词： Raman scattering

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Function-based question classification for general QA

Function-based question classification for general QA

引用

Conference on Empirical Methods in Natural Language Processing, EMNLP 2010

作者： Bu, Fan Zhu, Xingwei Hao, Yu Zhu, Xiaoyan State Key Laboratory of Intelligent Technology and Systems Tsinghua Department of Computer Sci. and Tech. Tsinghua University China

ISBN: (纸本)1932432868

In contrast with the booming increase of internet data, state-of-art QA (question answering) systems, otherwise, concerned data from specific domains or resources such as search engine snippets, online forums and Wikipedia in a somewhat isolated way. Users may welcome a more general QA system for its capability to answer questions of various sources, integrated from existed specialized sub-QA engines. In this framework, question classification is the primary task. However, the current paradigms of question classification were focused on some specified type of questions, i.e. factoid questions, which are inappropriate for the general QA. In this paper, we propose a new question classification paradigm, which includes a question taxonomy suitable to the general QA and a question classifier based on MLN (Markov logic network), where rule-based methods and statistical methods are unified into a single framework in a fuzzy discriminative learning approach. Experiments show that our method outperforms traditional question classification approaches. © 2010 Association for Computational Linguistics.

关键词： Search engines

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