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The Tourism-Specific Sentiment Vector Construction Based on Kernel Optimization Function

Procedia Computer science 2017年 122卷 1162-1167页

作者： Luyao Zhu Wei Li Kun Guo Yong Shi Yuanchun Zheng School of Economics and Management University of Chinese Academy of Sciences Beijing China Fictitious Economy & Data Science Research Center Chinese Academy of Sciences Beijing China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing China

Sentiment analysis in tourism domain has drawn much attention in past few years, which calls for more precise sentiment word embedding method. The article proposes a kernel optimization function for sentiment word embedding. And the method aims at integrating the semantic information, statistics information and sentiment information and maintains the similarity between sentiment words in terms of sentiment orientation. The experiment result shows that the optimal sentiment vectors successfully extract the features in terms of sentiment information and the difference between concretization and abstraction of a sentiment words.

关键词： kernel function sentiment vector word embedding sentiment analysis

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Decision trees for instance transfer 5th

Decision trees for instance transfer

引用

5th International Symposium on Conformal and Probabilistic Prediction with Applications, COPA 2016

作者： Zhou, Shuang Smirnov, Evgueni Schoenmakers, Gijs Peeters, Ralf Department of Data Science and Knowledge Engineering Maastricht University P.O. Box 616 Maastricht6200 MD Netherlands

ISBN: (纸本)9783319333946

Instance-transfer learning has emerged as a promising learning framework to boost performance of predictive models for a target domain by exploiting data from source domains. The success of the framework depends on the relevance of the source data to the target data. This paper proposes a decision-tree approach for instance transfer when the source and target data are relevant with respect to a strict subset of input features. Experimental results on real-world data sets demonstrate that the proposed approach outperforms existing instance-transfer approaches when the source and target data are partially related. © Springer International Publishing Switzerland 2016.

关键词： Decision trees

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A structured view on weighted counting with relations to counting, quantum computation and applications

arXiv

引用

arXiv 2017年

作者： de Campos, Cassio P. Stamoulis, Georgios Weyland, Dennis Queen’s University Belfast United Kingdom Department of Information and Computing Sciences Utrecht University Netherlands Department of Data Science & Knowledge Engineering Maastricht University Netherlands Google Zürich Switzerland

Weighted counting problems are a natural generalization of counting problems where a weight is associated with every computational path of polynomial-time non-deterministic Turing machines and the goal is to compute the sum of the weights of all paths (instead of just computing the number of accepting paths). Useful closure properties and plenty of applications make weighted counting problems interesting. The general definition of these problems captures even undecidable problems, but it turns out that obtaining an exponentially small additive approximation is just as hard as solving conventional counting problems. In many cases such an approximation is sufficient and working with weighted counting problems tends to be very convenient. We present a structured view on weighted counting by defining classes that depend on the range of the function that assigns weights to paths and by showing the relationships between these different classes. These classes constitute generalizations of the usual counting problems. Weighted counting allows us to easily cast a number of famous results of computational complexity in its terms, especially regarding counting and quantum computation. Moreover, these classes are flexible enough and capture the complexity of various problems in fields such as probabilistic graphical models and stochastic combinatorial optimization. Using the weighted counting terminology and our results, we are able to simplify and answer some open questions in those fields. Copyright © 2017, The Authors. All rights reserved.

关键词： Turing machines

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Accuracy estimation of link-based similarity measures and its application

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Frontiers of Computer science 2016年第1期10卷 113-123页

作者： Yinglong ZHANG Cuiping LI Chengwang XIE Hong CHEN School of Software East China Jiaotong University Nanchang 330045 China Key Laboratory of Data Engineering and Knowledge Engineering of Ministry of Education and Department of Computer Science Renmin University of China Beijing 100872 China Intelligent Optimization and Information Processing Laboratory East China Jiaotong University Nanchang 330013 China

Link-based similarity measures play a significant role in many graph based applications. Consequently, mea- suring node similarity in a graph is a fundamental problem of graph data mining. Personalized PageRank （PPR） and Sim- Rank （SR） have emerged as the most popular and influen- tial link-based similarity measures. Recently, a novel link- based similarity measure, penetrating rank （P-Rank）, which enriches SR, was proposed. In practice, PPR, SR and P-Rank scores are calculated by iterative methods. As the number of iterations increases so does the overhead of the calcula- tion. The ideal solution is that computing similarity within the minimum number of iterations is sufficient to guaran- tee a desired accuracy. However, the existing upper bounds are too coarse to be useful in general. Therefore, we focus on designing an accurate and tight upper bounds for PPR, SR, and P-Rank in the paper. Our upper bounds are designed based on the following intuition： the smaller the difference between the two consecutive iteration steps is, the smaller the difference between the theoretical and iterative similar- ity scores becomes. Furthermore, we demonstrate the effec- tiveness of our upper bounds in the scenario of top-k similar nodes queries, where our upper bounds helps accelerate the speed of the query. We also run a comprehensive set of exper- iments on real world data sets to verify the effectiveness and efficiency of our upper bounds.

关键词： personalized PageRank SimRank P-Rank up-per bound

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The Linked data Mining Challenge 2016 5

The Linked Data Mining Challenge 2016

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5th Joint Workshop on data Mining and knowledge Discovery meets Linked Open data and the 1st International Workshop on Completing and Debugging the Semantic Web, Know@LOD 2016 and CoDeS 2016

作者： Ristoski, Petar Paulheim, Heiko Svátek, Vojtěch Zeman, Václav University of Mannheim Research Group Data and Web Science Germany University of Economics Department of Information and Knowledge Engineering Prague Czech Republic

The 2016 edition of the Linked data Mining Challenge, conducted in conjunction with Know@LOD 2016, has been the fourth edition of this challenge. This year's dataset collected music album ratings, where the task was to classify well and badly rated music albums. The best solution submitted reached an accuracy of almost 92:5%, which is a clear advancement over the baseline of 69:38%.

关键词： Linked data

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Predicting visual features from text for image and video caption retrieval

arXiv

引用

arXiv 2017年

作者： Dong, Jianfeng Li, Xirong Snoek, Cees G.M. College of Computer Science and Technology Zhejiang University Hangzhou310027 China Key Lab of Data Engineering and Knowledge Engineering School of Information Renmin University of China Beijing100872 China Informatics Institute University of Amsterdam Amsterdam1098 XH Netherlands

This paper strives to find amidst a set of sentences the one best describing the content of a given image or video. Different from existing works, which rely on a joint subspace for their image and video caption retrieval, we propose to do so in a visual space exclusively. Apart from this conceptual novelty, we contribute Word2VisualVec, a deep neural network architecture that learns to predict a visual feature representation from textual input. Example captions are encoded into a textual embedding based on multi-scale sentence vectorization and further transferred into a deep visual feature of choice via a simple multi-layer perceptron. We further generalize Word2VisualVec for video caption retrieval, by predicting from text both 3-D convolutional neural network features as well as a visual-audio representation. Experiments on Flickr8k, Flickr30k, the Microsoft Video Description dataset and the very recent NIST TrecVid challenge for video caption retrieval detail Word2VisualVec's properties, its benefit over textual embeddings, the potential for multimodal query composition and its state-of-the-art results. Copyright © 2017, The Authors. All rights reserved.

关键词： Forecasting

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Semantics and computation of the evolution of hybrid systems with ariadne 14

Semantics and computation of the evolution of hybrid systems...

引用

14th International Conference on Computability and Complexity in Analysis, CCA 2017

作者： Collins, Pieter Bresolin, Davide Gonzalez, Sanja Živanovic Geretti, Luca Villa, Tiziano Department of Data Science and Knowledge Engineering Maastricht University Bouillonstraat 8-10 Postbus 616 6200MD Netherlands Dipartimento di Informatica Università di Bologna Mura Anteo Zamboni 7 Bologna40126 Italy Department of Mathematics and Computer Science Barry University 11300 NE 2nd Avenue Miami ShoresFL33161-6695 United States Dipartimento di Informatica Università Degli Studi di Verona Strada le Grazie 15 Verona37134 Italy

In this talk we will present material on the semantics, computability, and algorithms for the evolution of hybrid dynamical systems, and an overview of the tool Ariadne for verification of hybrid systems [1]. Hybrid systems are characterised by undergoing continuous evolution interspersed by discrete jumps. They exhibit all the complexities of finite automata, nonlinear dynamic systems and differential equations, and are extremely difficult to analyze. We will consider hybrid systems in which the continuous dynamics is given by a differential equation x = f(x), with discrete jumps x' = ri(x) which occur as soon as a guard condition gi(x) ≥ 0 is activated. It is clear that the evolution of a hybrid system undergoes discontinuities, but since only continuous functions are computable, it is not clear to what extent, if any, it is possible to perform a rigorous analysis of a hybrid system. We will first show that we can define lower and upper semantics of evolution under which it is possible to compute reachable sets, and that away from discontinuity points (such as grazing or corner collision points), these semantics agree [2]. In order to perform reachability analysis, it is necessary to define the evolution over bounded initial sets of states. We show that this can be done using the operations of range, compose, flow and solve operations on functions. We will see that constrained image sets of the form {f(x) | x ∈ D | g(x) ∈ C}, are sufficient to express the evolution exactly, except for the case of degenerate (non-transverse) cross- ings [3]. The flow operation is the most computationally demanding, and we will give some details of the implementation and efficiency considerations [4]. We will give examples of reachability analysis in Ariadne, including electrical power converters and heating systems. Finally, we will outline some areas of active research, including differential inclusions [5] and modular reasoning. © 2017 14th International Conference on Computabili

关键词： Hybrid systems

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Random analysis of statistical rough set

Random analysis of statistical rough set

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2016 International Conference on Machine Learning and Cybernetics, ICMLC 2016

作者： Tsang, Eric C.C. Zhao, Su-Yun Faculty of Information Technology Macau University of Science and Technology C-Macau China Key Laboratory of Data Engineering and Knowledge Engineering MOE Renmin University of China Beijing China

ISBN: (纸本)9781509003891

Attribute reduction is an inevitable problem in machine learning and statistical learning. To improve the traditional rough set reduction, statistical rough sets is then proposed by introducing random sampling into the rough approximation. Random sampling is the main contribution of statistical rough sets. As a result, it is necessary to analyze the randomness of statistical rough sets. In this paper, we analyze and demonstrate the influence of the randomness in the process of attribute reduction by a large number of experiments to test the effectiveness and stability of the random sampling. © 2016 IEEE.

关键词： Sampling

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Towards multi-target search of semantic association 6th

Towards multi-target search of semantic association

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6th Joint International Conference on Semantic Technology, JIST 2016

作者： Zhang, Xiang Lv, Yulian School of Computer Science and Engineering Southeast University Nanjing China Key Laboratory of Data Engineering and Knowledge Services Nanjing University Nanjing China Southeast University Suzhou China

ISBN: (纸本)9783319501116

Semantic association represents group relationship among objects in linked data. Searching semantic associations is complicated, which involves the search of multiple objects and the search of their group relationships simultaneously. In this paper, we propose this kind of search as a multi-target search, and we compare it to traditional search tasks, which we classify as single-target search. A novel search model is introduced, and the notion of virtual document is used to extract linguistic information of semantic associations. Multi-target search is finally fulfilled by a PageRank-like ranking scheme and a top-K selection policy considering object affinity. Experiments show that our approach is effective in improving retrieval precision on semantic associations. © Springer International Publishing AG 2016.

关键词： Linked data

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Hierarchical Task Network Plan Reuse for video games

Hierarchical Task Network Plan Reuse for video games

引用

IEEE Symposium on Computational Intelligence and Games, CIG

作者： Dennis J. N. J. Soemers Mark H. M. Winands Department of Data Science and Knowledge Engineering Maastricht University

ISBN: (纸本)9781509018840

Hierarchical Task Network Planning is an Automated Planning technique. It is, among other domains, used in Artificial Intelligence for video games. Generated plans cannot always be fully executed, for example due to nondeterminism or imperfect information. In such cases, it is often desirable to re-plan. This is typically done completely from scratch, or done using techniques that require conditions and effects of tasks to be defined in a specific format (typically based on First-Order Logic). In this paper, an approach for Plan Reuse is proposed that manipulates the order in which the search tree is traversed by using a similarity function. It is tested in the SimpleFPS domain, which simulates a First-Person Shooter game, and shown to be capable of finding (optimal) plans with a decreased amount of search effort on average when re-planning for variations of previously solved problems.

关键词： Planning Compounds Games Silicon Engines Optimization C++ languages

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