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23rd International Conference on Computational Linguistics, Coling 2010

作者： Liu, Bing Qian, Longhua Wang, Hongling Zhou, Guodong Jiangsu Provincial Key Lab for Computer Information Processing Technology School of Computer Science and Technology Soochow University China

Recent kernel-based PPI extraction systems achieve promising performance because of their capability to capture structural syntactic information, but at the expense of computational complexity. This paper incorporates dependency information as well as other lexical and syntactic knowledge in a feature-based framework. Our motivation is that, considering the large amount of biomedical literature being archived daily, feature-based methods with comparable performance are more suitable for practical applications. Additionally, we explore the difference of lexical characteristics between biomedical and newswire domains. Experimental evaluation on the AIMed corpus shows that our system achieves comparable performance of 54.7 in F1-Score with other state-of-the-art PPI extraction systems, yet the best performance among all the feature-based ones.

关键词： Syntactics

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Dependency-driven anaphoricity determination for coreference resolution

Dependency-driven anaphoricity determination for coreference...

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23rd International Conference on Computational Linguistics, Coling 2010

作者： Kong, Fang Zhou, Guodong Qian, Longhua Zhu, Qiaoming JiangSu Provincial Key Lab for Computer Information Processing Technology School of Computer Science and Technology Soochow University China

This paper proposes a dependency-driven scheme to dynamically determine the syntactic parse tree structure for tree kernel- based anaphoricity determination in coreference resolution. Given a full syntactic parse tree, it keeps the nodes and the paths related with current mention based on constituent dependencies from both syntactic and semantic perspectives, while removing the noisy information, eventually leading to a dependency-driven dynamic syntactic parse tree (D-DSPT). Evaluation on the ACE 2003 corpus shows that the D-DSPT outperforms all previous parse tree structures on anaphoricity determination, and that applying our anaphoricity determination module in coreference resolution achieves the so far best performance.

关键词： Syntactics

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Combining event-level and cross-event semantic information for event-oriented relation classification by SCNN 15th

Combining event-level and cross-event semantic information f...

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15th China National Conference on Chinese Computational Linguistics, CCL 2016 and 4th International Symposium on Natural Language processing Based on Naturally Annotated Big Data, NLP-NABD 2016

作者： Ding, Siyuan Hong, Yu Zhu, Shanshan Yao, Jianmin Zhu, Qiaoming Provincial Key Laboratory of Computer Information Processing Technology Soochow University Suzhou China

ISBN: (纸本)9783319476735

Previous researches on event relation classification primarily rely on lexical and syntactic features. In this paper, we use a Shallow Convolutional Neural Network (SCNN) to extract event-level and cross-event semantic features for event relation classification. On the one hand, the shallow structure alleviates the over-fitting problem caused by the lack of diverse relation samples. On the other hand, the utilization and combination of event-level and cross-event semantic information help improve relation classification. The experimental results show that our approach outperforms the state of the art. © Springer International Publishing AG 2016.

关键词： Neural networks

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A topic-based reordering model for statistical machine translation 3

A topic-based reordering model for statistical machine trans...

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3rd CCF Conference on Natural Language processing and Chinese Computing, NLPCC 2014

作者： Wang, Xing Xiong, Deyi Zhang, Min Hong, Yu Yao, Jianmin Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou China

ISBN: (纸本)9783662459232

Reordering models are one of essential components of statistical machine translation. In this paper, we propose a topic-based reordering model to predict orders for neighboring blocks by capturing topic-sensitive reordering patterns. We automatically learn reordering examples from bilingual training data, which are associated with document-level and word-level topic information induced by LDA topic model. These learned reordering examples are used as evidences to train a topic-based reordering model that is built on a maximum entropy (MaxEnt) classifier. We conduct large-scale experiments to validate the effectiveness of the proposed topic-based reordering model on the NIST Chinese-to-English translation task. Experimental results show that our topic-based reordering model achieves significant performance improvement over the conventional reordering model using only lexical information. © Springer-Verlag Berlin Heidelberg 2014.

关键词： computer aided language translation

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A survey on video-based vehicle behavior analysis algorithms

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Journal of Multimedia 2012年第3期7卷 223-230页

作者： Wu, Jian Cui, Zhi-Ming Chen, Jian-Ming Zhang, Guang-Ming The Institute of Intelligent Information Processing and Application Soochow University Suzhou 215006 China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou 215006 China Jiangsu Yihe Traffic Engineering Co. Ltd Suzhou 215002 China

Analysis of the Vehicle Behavior is mainly to analyze and identify the vehicles' motion pattern, and describe it by the use of natural language. It is a considerable challenge to analyze and describe the vehicles' behavior in a complex scene. This paper first hackles the development history of the intelligent transportation system and analysis of vehicles' behavior, and then conducts an indepth analysis of current situation of vehicle behavior analysis from the video processing, video analysis and video understanding, summarizes the achieved results and the key technical problems, and prospects the future development of vehicle behavior analysis. © 2012 ACADEMY PUBLISHER.

关键词： Video signal processing

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Coverage Enhancement Strategy Based on Novel Perception and Co-evolution for Multimedia Sensor Networks

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电子学报(英文版) 2013年第1期22卷 135-140页

作者： XIAO Fu WANG Jing SUN Lijuan WANG Ruchuan GUO Jian College of Computer Nanjing University of Posts and Telecommunications Nanjing 210003 China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Nanjing 210003 China Key Laboratory of Broadband Wireless Communication and Sensor Network Technology Ministry of Education Nanjing 210003 China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou 215006 China

Coverage enhancement is one of the hot research topics in wireless multimedia sensor net- works. A novel Coverage-enhancing algorithm based on three-dimensional Directional perception and co-evolution (DPCCA) is proposed in multimedia sensor networks on the basis of the model whose pitch angle and deviation angle can be adjusted. Based on the proposed elliptical cone sensing model, we can derive the coverage area of the node and calculate the optimal pitch angle according the information of monitoring area and the nodes, and then the deviation angle is optimized based on co-evolution al- gorithm, which eliminate the overlapped and blind sensing area effectively. A set of simulations demonstrate the ef- fectiveness of our algorithm in coverage ratio.

关键词：无线多媒体传感器网络网络覆盖协同进化感知增强算法网络工程测量模型

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Restoration-enhanced reversible information steganography network for CT images in the Internet of Medical Things

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IEEE Transactions on Instrumentation and Measurement 2025年

作者： Chen, Kai Chen, Yang Coatrieux, Gouenou Wu, Jiasong Coatrieux, Jean-Louis School of Cyber Science and Engineering Southeast University Nanjing210096 China Southeast University Nanjing210096 China IMT Atlantique Inserm LaTIM UMR1101 Brest29000 France Laboratoire Traitement du Signal et de L'Image Université de Rennes 1 RennesF-35000 France Centre de Recherche en Information Biomédicale Sino-francais Rennes35042 France National Institute for Health and Medical Research RennesF-35000 France Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing School of Computer Science and Engineering Southeast University Nanjing210096 China Jiangsu Key Laboratory of Molecular and Functional Imaging Department of Radiology Zhongda Hospital Southeast University Nanjing210009 China Ministry of Education Nanjing210096 China Ministry of Education Nanjing210096 China

Smart health and emotional care powered by the Internet of Medical Things (IoMT) are revolutionizing the healthcare industry by adopting several technologies related to multimodal physiological data collection, communication, intelligent automation, and efficient manufacturing. The security of medical images containing private patient information distributed in IoMT is of paramount importance in social communication, and guaranteeing their reliability has attracted widespread attention. Some existing medical image steganography methods fail to guarantee the original image's integrity, authenticity, and lossless restoration. To solve this problem, a restoration-enhanced reversible adaptive information steganography network, MediSR-Net, is proposed. Specifically, MediSR-Net consists of an analysis module and an encoding module. The image degradation-restoration strategy is exploited by the analysis module to improve the effectiveness of information embedding and perceptual field to reduce the error of pixel prediction. An information distillation and attention machine-based approach are adopted to achieve degradation-restoration of CT medical images at any scale and focus more on the degradation-restoration effect with small magnification factors. The encoding module is responsible for the embedding and extraction of privacy information. Ten state-of-the-art are compared with the MediSR-Net method concerning steganographic effect, embedding rate, distortion analysis, and prediction error. The qualitative and quantitative results demonstrate the effectiveness and superiority of MediSR-Net. © 1963-2012 IEEE.

关键词： Steganography

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Assigning method for decision power based on linguisitc 2-tuple judgment matrices

Journal of Software

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Journal of Software 2011年第3期6卷 508-515页

作者： Zhang, Xixiang Yue, Guangxue Zheng, Xiajie Yu, Fei Business School of Jiaxing University Jiaxing China Mathematics and Information Engineering School of Jiaxing University Jiaxing China Department of Computer Science and Technology Huaihua University Huaihua China Jiangsu Provincial Key Laboratory for Computer Information Processing Technology Soochow University Soochow China

Decision power is very important in group decision making, which effects the final decision making result. When there exists uncertainty in group decision making, it is easy for an expert to express his/her preferences using fuzzy linguistic term such as 'good', 'very good'. The linguistic 2-tuple representation model was selected to represent fuzzy linguistic term for its accuracy in representing fuzzy terms and less loss of information in processing. To obtain the objective decision power in group decision making where the experts express their opinions using linguistic 2-tuple judgment matrices, the paper put forward a new approach to assign decision power based on element consistency level. And the method for calculating the contribution degree of each expert was also provided. The proposed method thought of calculating the different weight to aggregate different element according to the element consistent level. An illustrated example was used to demonstrate the proposed method. And a GDSS based on the method was also developed to simplify the application in ***. The GDSS was applied to building non-financial performance system for listed company in small and medium enterprise board in China, which showed its reasonability. © 2011 ACADEMY PUBLISHER.

关键词： Linguistics

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Improve tree kernel-based event pronoun resolution with competitive information

Improve tree kernel-based event pronoun resolution with comp...

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22nd International Joint Conference on Artificial Intelligence, IJCAI 2011

作者： Kong, Fang Zhou, Guodong JiangSu Provincial Key Lab. for Computer Information Processing Technology School of Computer Science and Technology Soochow University China

ISBN: (纸本)9781577355120

Event anaphora resolution plays a critical role in discourse analysis. This paper proposes a tree kernel- based framework for event pronoun resolution. In particular, a new tree expansion scheme is introduced to automatically determine a proper parse tree structure for event pronoun resolution by considering various kinds of competitive information related with the anaphor and the antecedent candidate. Evaluation on the OntoNotes English corpus shows the appropriateness of the tree kernel-based framework and the effectiveness of competitive information for event pronoun resolution.

关键词： Trees (mathematics)

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A tree kernel-based unified framework for Chinese zero anaphora resolution

A tree kernel-based unified framework for Chinese zero anaph...

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Conference on Empirical Methods in Natural Language processing, EMNLP 2010

作者： Kong, Fang Zhou, Guodong JiangSu Provincial Key Lab. for Computer Information Processing Technology School of Computer Science and Technology Soochow University China

ISBN: (纸本)1932432868

This paper proposes a unified framework for zero anaphora resolution, which can be divided into three sub-tasks: zero anaphor detection, anaphoricity determination and antecedent identification. In particular, all the three sub-tasks are addressed using tree kernel-based methods with appropriate syntactic parse tree structures. Experimental results on a Chinese zero anaphora corpus show that the proposed tree kernel-based methods significantly outperform the feature-based ones. This indicates the critical role of the structural information in zero anaphora resolution and the necessity of tree kernel-based methods in modeling such structural information. To our best knowledge, this is the first systematic work dealing with all the three sub-tasks in Chinese zero anaphora resolution via a unified framework. Moreover, we release a Chinese zero anaphora corpus of 100 documents, which adds a layer of annotation to the manually-parsed sentences in the Chinese Treebank (CTB) 6.0. © 2010 Association for Computational Linguistics.

关键词： Trees (mathematics)

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