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检索条件"机构=Laboratory of Intelligent Information ProcessingInstitute of Computing Technology"

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Improving Tree-to-Tree Translation with Packed Forests

Improving Tree-to-Tree Translation with Packed Forests

引用

Joint Conference of the 47th Annual Meeting of the Association for Computational Linguistics and 4th International Joint Conference on Natural Language Processing of the AFNLP, ACL-IJCNLP 2009

作者： Liu, Yang Lü, Yajuan Liu, Qun Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences P.O. Box 2704 Beijing100190 China

ISBN: (纸本)9781617382581

Current tree-to-tree models suffer from parsing errors as they usually use only 1-best parses for rule extraction and decoding. We instead propose a forest-based tree-to-tree model that uses packed forests. The model is based on a probabilistic synchronous tree substitution grammar (STSG), which can be learned from aligned forest pairs automatically. The decoder finds ways of decomposing trees in the source forest into elementary trees using the source projection of STSG while building target forest in parallel. Comparable to the state-of-the-art phrase-based system Moses, using packed forests in tree-to-tree translation results in a significant absolute improvement of 3.6 BLEU points over using 1-best trees. © 2009 ACL and AFNLP.

关键词： Decoding

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Joint Decoding with Multiple Translation Models

Joint Decoding with Multiple Translation Models

引用

Joint Conference of the 47th Annual Meeting of the Association for Computational Linguistics and 4th International Joint Conference on Natural Language Processing of the AFNLP, ACL-IJCNLP 2009

作者： Liu, Yang Mi, Haitao Feng, Yang Liu, Qun Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences P.O. Box 2704 Beijing100190 China

ISBN: (纸本)9781617382581

Current SMT systems usually decode with single translation models and cannot benefit from the strengths of other models in decoding phase. We instead propose joint decoding, a method that combines multiple translation models in one decoder. Our joint decoder draws connections among multiple models by integrating the translation hypergraphs they produce individually. Therefore, one model can share translations and even derivations with other models. Comparable to the state-of-the-art system combination technique, joint decoding achieves an absolute improvement of 1.5 BLEU points over individual decoding. © 2009 ACL and AFNLP.

关键词： Decoding

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THE EFFECT OF SCALE TRANSFORMATION FOR HYPER SURFACE CLASSIFICATION METHOD

THE EFFECT OF SCALE TRANSFORMATION FOR HYPER SURFACE CLASSIF...

引用

2009 International Conference on Machine Learning and Cybernetics(2009机器学习与控制论国际会议)

作者： QING HE ZHONG-ZHI SHI XU-DONG MA FU-ZHEN ZHUANG The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese

Hyper Surface Classification (HSC), which is based on Jordan Curve Theorem in Topology, has been proven to be a simple and effective method for classifying a large database in our previous work. In this paper, through theoretical analysis, we find that different scales may affect the training process of HSC, which influences its classification performance. To investigate the impact and find a suitable scale, the scale transformation of HSC is studied. The experimental results show that the accuracy increases with the shrinkage of the scale, but the effect is tiny. Furthermore, we find that some samples become inconsistent and repetitious when the scale is adequately small, because of the powerlessly providing enough precision by the data type of computer. Fortunately, HSC can get a high performance with common scales as experiments exhibit.

关键词： Hyper Surface Classification HSC Scale Transformation

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Unusual Pattern Detection Based on Hyper Surface and Minimum Spanning Tree

Unusual Pattern Detection Based on Hyper Surface and Minimum...

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2009 IEEE International Conference on information and Automation(2009年 IEEE信息与自动化国际学术会议)

作者： Qing He Weizhong Zhao Jincheng Li Zhongzhi Shi The Key Laboratory of Intelligent Information Processing Institute of Computing TechnologyChinese A The Key Laboratory of Intelligent Information Processing Institute of Computing TechnologyChinese A

More analysis has been done to discover the meaningful unusual patterns which may mean fraud or anomaly. In this paper, a novel unsupervised approach for discovering meaningful unusual observations is proposed. We firstly apply an unsupervised version of Hyper Surface Classification (HSC) algorithm to gain the separating hyper surface. It needs no domain knowledge but can not discover the local unusual pattern. To solve this problem, we additionally search the Minimum Spanning Tree (MST). Given the domain knowledge, a process of subdividing is proposed to detect unusual pattern in each Minimum Spanning Tree. Experimental results show that our approach can detect unusual patterns effectively, even some of which are overlooked by using the traditional clustering and outlier detection algorithms.

关键词： Detection algorithms Credit cards Pattern analysis Face detection Predictive models Automation Laboratories information processing Computers information analysis

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Emotion modeling in autonomic computing

Emotion modeling in autonomic computing

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2009 8th IEEE International Conference on Cognitive Informatics, ICCI 2009

作者： Donglei, Zhang Pan, Yu Shil, Zhongzhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China Graduate University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781424446421

Autonomic unit is the essential element and the most basic component of autonomic systems. Having the ability of handling emotions is considered to make autonomic unit more intelligent, more communicative and more social. In this article we try to build an emotion model for agents that act as autonomic units in autonomic systems and can self-manage its behavior to achieve the best performance. From four levels we define four models progressively and the top-level model is defined on the foundation of the three below, combining them as a unity. By given arousing axioms theoretically of several typical emotions, the model is proved to be effective for reasoning emotions of autonomic unit, and can be easily extended for dealing with other type of emotions. On this base an architecture of emotional autonomic unit is described. An example shows that emotion plays an important role in intelligent decision making, which proves the validity of the model and the special function in practice. © 2009 IEEE.

关键词： Decision making

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Detection of front-view vehicle with occlusions using AdaBoost

Detection of front-view vehicle with occlusions using AdaBoo...

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2009 International Conference on information Engineering and Computer Science, ICIECS 2009

作者： Wu, Chunpeng Duan, Lijuan Miao, Jun Fang, Faming Wang, Xuebin College of Computer Sciences Beijing University of Technology Beijing 100124 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology China Academy of Sciences Beijing 100190 China

ISBN: (纸本)9781424449941

In this paper, we propose a vehicle detection method based on AdaBoost. We focus on the detection of front-view car and bus with occlusions on highway. Samples with different occlusion situations are selected into the training set. By using basic and rotated Haar-like features extracted from the samples in the set, we train an AdaBoost-based cascade vehicle detector. The performance tests on static images and short time videos show that (1)our approach detects cars more effectively than buses (2)the real-time detection of our method on video proceeds at 30 frames per second. ©2009 IEEE.

关键词： Adaptive boosting

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Multi-Agent Based Bioinformatics Integration Using Distributed Dynamic Description Logics

Multi-Agent Based Bioinformatics Integration Using Distribut...

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Fifth International Conference on Semantics,Knowledge and Grid(第五届语义、知识与网格国际会议 SKG 2009)

作者： Zhuxiao Wang Dapeng Zhang Zhongzhi Shi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Acad Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Acad

Integration of bioinformatics data repositories is a challenging task in which data sets are usually heterogeneous in structure and are often distributed across multiple, autonomously maintained databases. In this context, we present an innovative system which coordinates bioinformatics data by combining P2P data integration paradigm and Distributed Dynamic Description Logics (D3L) on top of Multi- Agent System infrastructure. We define the semantics and syntax of D3L, and propose a distributed consistency checking algorithm for realizing the intelligent query with logical reasoning function and decomposing large tasks to sub-tasks that could be tackled by different agents. Finally, we introduce a prototype implementation and present its evaluation. The results indicate that the proposed approach achieves excellent robustness and satisfactory performance.

关键词： bioinformatics integration agent technology Distributed Dynamic Description Logics

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Complementary Relations and Their Concept Lattices in Relational Databases

Complementary Relations and Their Concept Lattices in Relati...

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Fifth International Conference on Semantics,Knowledge and Grid(第五届语义、知识与网格国际会议 SKG 2009)

作者： Yuxia Lei Yuefei Sui Cungen Cao Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Acad Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS Beijing

The storage of data is a key issue of information systems, which is an important foundation for data query and data mining. Relational database model has been proven to be a very useful data-storage technique. As information is stored as data in relational databases, the induction of concepts from data is a pivotal topic in the data mining field. Formal Concept Analysis (FCA) turns out to be a perfect instrument for a meaningful and conceptual exploration of the stored data. In FCA, conceptual scaling provides a complete framework for transforming any many-valued context (i.e., relation/table) into a context (called a derived context), in which each manyvalued attribute is given a scale. The attributes in a scale basically describe meaningful features of the values of the initial attribute. From the logical point of view, complement operation plays a very important role in relational databases and data query systems. In this paper, we provide the connections between the concepts of binary relations and those of complementary binary relations, and propose an approach toward normalizing (complementary) scales, i.e., each (complementary) scale can be represented by a set of statements. One advantage of normalizing scales is to avoid generating huge derived relations, and hence this approach reduces storage cost. By the normalization, the concept lattice of the complement of a derived relation is reduced to a combination of the concept lattice of the derived relation and a set of statements.

关键词： relational databases complementary relations concept lattices normalized scales

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The Logical Operations on Relations, Scales and Concept Lattices

The Logical Operations on Relations, Scales and Concept Latt...

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Fifth International Conference on Semantics,Knowledge and Grid(第五届语义、知识与网格国际会议 SKG 2009)

作者： Yuxia Lei Cungen Cao Yuefei Sui Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Acad Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Acade

Object-attribute-value-relationships, which are a frequently used data structure to code real-world problems, are formalized via many-valued contexts in Formal Concept Analysis (FCA). The aim of FCA is to explore (formal) concepts from empirical data contexts. A concept of a context consists of two parts: the extent (objects the concept covers) and the intent (attributes describing the concept). From the logical point of view, the intent of each concept is a conjunction of some attributes. Similar to conjunction, negation and disjunction are also important logical operations of attributes or attributevalue pairs, which are common in human language. However, the classical FCA as a mathematical theory of concepts lacks a theory of negation and disjunction. In this paper, we take negation and disjunction into consideration in the process of constructing concepts, and hence obtain the following extended concepts: negative concepts of a context (i.e., a binary relation), negative concepts of a relation with some scales, V-concepts of a relation, V-concepts of a relation with some scales, logical concepts of a relation, and logical concepts of a relation with some scales. Compared with concepts in the classical FCA, the extended concepts mentioned above are more pertinent and more meaningful.

关键词： relational databases concept lattices logical operations negation disjunction conjunction

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CoBoost learning of visual categories with 1st and 2 nd order features from Google images 09

CoBoost learning of visual categories with 1st and 2 nd orde...

引用

17th ACM International Conference on Multimedia, MM'09, with Co-located Workshops and Symposiums

作者： Liu, Xi Shi, Zhiping Li, Zhixin Shi, Zhongzhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Graduate University of Chinese Academy of Sciences Beijing 100049 China

ISBN: (纸本)9781605586083

Conventional object recognition techniques rely heavily on manually annotated image datasets to achieve good performances. However, collecting high quality datasets is really laborious. In this paper, we propose a semi-supervised framework for learning visual categories from Google Images. The 1st and 2nd order features, which define bag of words representation and spatial relationship between local features respectively, make up an independent and redundant feature split. We then integrate a cotraining algorithm CoBoost with these two features. We create two boosting classifiers based on the 1st and 2nd order features respectively in the training, during which one classifier provides labels for the other. Besides, the 2nd order features are generated dynamically rather than extracted exhaustively to avoid high computation. An active learning technique is also introduced to further improve the performance. We evaluate our method on the benchmark datasets, showing results competitive with the state-of-the-art unsupervised approaches and some supervised techniques. Copyright 2009 ACM.

关键词： Object recognition

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