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Semi-supervised learning for semantic relation classification using stratified sampling strategy

Semi-supervised learning for semantic relation classificatio...

2009 Conference on Empirical Methods in Natural Language processing, EMNLP 2009, Held in Conjunction with ACL-IJCNLP 2009

作者： Qian, Longhua Zhou, Guodong Kong, Fang Zhu, Qiaoming Jiangsu Provincial Key Lab for Computer Information Processing Technology School of Computer Science and Technology Soochow University 1 Shizi Street Suzhou 215006 China

This paper presents a new approach to selecting the initial seed set using stratified sampling strategy in bootstrapping-based semi-supervised learning for semantic relation classification. First, the training data is partitioned into several strata according to relation types/subtypes, then relation instances are randomly sampled from each stratum to form the initial seed set. We also investigate different augmentation strategies in iteratively adding reliable instances to the labeled set, and find that the bootstrapping procedure may stop at a reasonable point to significantly decrease the training time without degrading too much in performance. Experiments on the ACE RDC 2003 and 2004 corpora show the stratified sampling strategy contributes more than the bootstrapping procedure itself. This suggests that a proper sampling strategy is critical in semi-supervised learning. © 2009 ACL and AFNLP.

关键词： Supervised learning

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Exploiting constituent dependencies for tree kernel-based semantic relation extraction

Exploiting constituent dependencies for tree kernel-based se...

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22nd International Conference on Computational Linguistics, Coling 2008

作者： Qian, Longhua Zhou, Guodong Kong, Fang Zhu, Qiaoming Qian, Peide Jiangsu Provincial Key Lab. for Computer Information Processing Technology School of Computer Science and Technology Soochow University 1 Shizi Street Suzhou 215006 China

ISBN: (纸本)9781905593446

This paper proposes a new approach to dynamically determine the tree span for tree kernel-based semantic relation extraction. It exploits constituent dependencies to keep the nodes and their head children along the path connecting the two entities, while removing the noisy information from the syntactic parse tree, eventually leading to a dynamic syntactic parse tree. This paper also explores entity features and their combined features in a unified parse and semantic tree, which integrates both structured syntactic parse information and entity-related semantic information. Evaluation on the ACE RDC 2004 corpus shows that our dynamic syntactic parse tree outperforms all previous tree spans, and the composite kernel combining this tree kernel with a linear state-of-the-art feature-based kernel, achieves the so far best performance. © 2008. Licensed under the Creative Commons.

关键词： Semantics

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Research of event pronoun resolution

Research of event pronoun resolution

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2011 International Conference on Asian Language processing, IALP 2011

作者： Zhang, Ning Kong, Fang Li, Peifeng JiangSu Provincial Key Lab. for Computer Information Processing Technology Soochow University Suzhou China School of Computer Science and Technology Soochow University Suzhou China

ISBN: (纸本)9780769545547

Event anaphora resolution plays an important role in discourse analysis. In comparison with general noun phrases, pronouns carry little information of themselves, resolving the event pronouns is a more difficult task. This paper proposes a machine learning-based framework for event pronoun resolution. All kinds of features, including both flat and structural features, are explored for event pronoun resolution. Experiments on OntoNotes corpus show that both flat and structural features are very effective for this task. © 2011 IEEE.

关键词： Learning systems

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Research on cross-document coreference of Chinese person name

Research on cross-document coreference of Chinese person nam...

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2011 International Conference on Asian Language processing, IALP 2011

作者： Ni, Ji Kong, Fang Li, Peifeng Zhu, Qiaoming JiangSu Provincial Key Lab. for Computer Information Processing Technology Soochow University Suzhou China School of Computer Science and Technology Soochow University Suzhou China

ISBN: (纸本)9780769545547

In reality, different persons often have the same person name. The Person Cross Document Co-reference Resolution is a task, which requires that all and only the textual mentions of an entity of type Person be individuated in a collection of text documents. In this paper, we implement a Chinese Person Name Cross Document Co-reference Resolution System. First, we utilize name identification module to recognize all person names of the texts, and then classify the document collection of same person name by rules preliminarily, and at last, we compute similarities of each classification based on VSM, according to the prior similarities, the system get the final classification results. We test the system on 30 usual Chinese names of the corpus provided by CLP, and average F measure is 85.9%. © 2011 IEEE.

关键词： information retrieval systems

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Research of noun phrase coreference resolution

Research of noun phrase coreference resolution

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2011 International Conference on Asian Language processing, IALP 2011

作者： Gao, Junwei Kong, Fang Li, Peifeng Zhu, Qiaoming JiangSu Provincial Key Lab. for Computer Information Processing Technology Soochow University Suzhou China School of Computer Science and Technology Soochow University Suzhou China

ISBN: (纸本)9780769545547

Coreference resolution is an important subtask in natural language processing systems. The process of it is to find whether two expressions in natural language refer to the same entity in the world. Machine learning approaches to this problem have been reasonably successful, operating primarily by recasting the problem as a classification task. A great deal of research has been done on this task in English, using approaches ranging from those based on linguistics to those based on machine learning. In Chinese, however, much less work has been done in this area. The lack of public resources is a big problem in the research of Chinese NLP. The other problem is that some features are more difficult to get than those features of English. In this paper, We present a noun phrase coreference system that refers to the work of Soon et al. (2001). We also explore the impact of various features on our system's performance. Experiments on the Chinese portion of OntoNotes 3.0 show that the platform achieves a good performance. © 2011 IEEE.

关键词： Natural language processing systems

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Classification rules mining based on SOFM networks

Classification rules mining based on SOFM networks

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MIPPR 2005: Geospatial information, Data Mining, and Applications

作者： Yao, Min Jiang, Zhiwei Shen, Bin College of Computer Zhejiang University Hangzhou 310027 China Provincial Key Lab. for Computer Information Processing Technology in Jiangsu Suzhou University Suzhou 215006 China

Self-organization feature mapping (SOFM) networks have strong ability for self-learning and self-adaptive. According to the characteristics of human thought, this paper constructed a kind of combined criterion, which may be used to guide the learning of self-organization feature mapping network. Then this paper presents subsection algorithm, amalgamation algorithm and dynamical adaptive algorithm for SOFM networks so as to solve a kind of problems of classification rule mining. Finally, a practical example shows its flexibility and practicability.

关键词： Self organizing maps

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Multi-Scale Cascaded with Cross-Attention Network-Based Deformation Vector Field Estimation for Motion-Compensated 4D-CBCT Reconstruction

IEEE Transactions on Computational Imaging

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IEEE Transactions on Computational Imaging 2025年

作者： Yuan, Peng Lyu, Fei Gao, Zhiqiang Yang, Chunfeng Hu, Dianlin Zhu, Jian Wu, Zhan Lyu, Tianling Zhao, Wei Dong, Jianmin Chen, Yang Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Ministry of Education Southeast University Nanjing210096 China Laboratory of Image Science and Technology Southeast University Nanjing210096 China Department of Computer Science Hong Kong Baptist University Hong Kong Hong Kong Academy for Advanced Interdisciplinary Science and Technology Zhejiang University of Technology Hangzhou China School of Physics Beihang University Beijing China Beihang Hangzhou Innovation Institute Zhejiang Hangzhou China School of Shandong Cancer Hospital and Institute Shandong First Medical University Shandong Academy of Medical Sciences Jinan Shandong China School of Information Engineering Xizang Minzu University Xianyang712082 China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing The Laboratory of Image Science and Technology The School of Computer Science and Engineering Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Ministry of Education Nanjing210096 China

Four-Dimensional Cone Beam Computed Tomography (4D-CBCT) imaging technology offers enhanced image quality and spatial resolution for intraoperative guidance, facilitating real-time tracking of tumor position changes during radiotherapy. However, this is still a task of great challenges due to insufficient projections at each respiratory phase after phase-sorting, and the image phases reconstructed directly from phase-sorted data are discrete and discontinuous. To generate high-quality 4D-CBCT deformation vector fields (DVFs), this paper leverages the preoperative static prior image to guide intraoperative dynamic sparse-view reconstruction images for reducing anatomical structure differences, ultimately achieving continuous and dynamic 4D-CBCT imaging. In this paper, we propose a Multi-scale Cascaded residual deformable vector field estimation framework based on Cross-attention in Motioncompensated 4D-CBCT reconstruction (MCCM), which combines Multi-Scale Cascaded residual registration network (MSCNet), Cross-Attention Enhanced feature Fusion (CAEF) module and Structure-Enhanced Motion-Compensated (SEMC) module: 1) the MCCM employs a multi-scale cascaded residual network strategy, merging multi-receptive fields and multi-resolution feature maps for large-scale internal changes. 2) the CAEF is embedded into MSC-Net to facilitate effective communication and learning between features and promote the flow of information. 3) the SEMC is developed to reduce artifacts after intraoperative CBCT sparse-view reconstruction, restore global lung motion changes and local details, and enhance structural information through image fusion. The proposed method has been evaluated using simulated and clinical datasets and has shown promising results by comparative experiment. Our approach exhibits significant improvements across various evaluation metrics. © 2024 IEEE.

关键词： Image fusion

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Assessment of rotamer library for protein structure prediction

Assessment of rotamer library for protein structure predicti...

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2nd International Conference on information Science and Engineering, ICISE2010

作者： Huang, Xu Lü, Qiang Miao, Dajun Qian, Peide School of Computer Science and Technology Soochow University Suzhou 215006 China Jiangsu Provincial Key Lab for Information Processing Technologies Suzhou 215006 China

ISBN: (纸本)9781424480968

Side chain packing is a crucial step for protein structure prediction. And the quality of rotamer library has important implications in side chain packing. This paper assess dunbrack rotamer libraries and simple sampling rotamer libraries for certain structures by comparing the native structure and solutions come from rotamer libraries. Results of eleven hard targets in CASP9 show that the best solution is near to the native structure. Otherwise, there is no native structure in protein structure *** compare the best solutions which choosing by lower RMSD and energy, it shows that the best solution which choosing by lower energy is near to the best solution which choosing by low RMSD. So, in the case of unknown native structure, the energy guided choosing is necessary, but it must adopt some other method such as clustering to select the best solution. © 2010 IEEE.

关键词： Chains

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Dependency tree-based anaphoricity determination for coreference resolution

Dependency tree-based anaphoricity determination for corefer...

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International Conference on Asian Language processing

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

ISBN: (纸本)9780769542881

This paper proposes a new scheme to determine the tree span structure for tree kernel-based anaphoricity determination in coreference resolution. Given a sentence and current mention, it gets all the dependencies, encodes the order of all the words, constructs the corresponding dependency tree and keeps the nodes and the paths related with current mention based on constituent dependencies, while removing the noisy information, eventually leading to a dynamical dependency tree (DDT). Evaluation on the ACE 2003 corpus shows that the DDT is effective for anaphoricity determination, and that applying our anaphoricity determination module in coreference resolution achieves the improvement of performance. © 2010 IEEE.

关键词： Anaphoricity determination Coreference resolution Dependency tree Coreference Resolution DDT DEPENDENCE

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Verifiable Fully Homomorphic Encryption scheme

Verifiable Fully Homomorphic Encryption scheme

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2012 2nd International Conference on Consumer Electronics, Communications and Networks, CECNet 2012

作者： Jin, Fangyuan Zhu, Yanqin Luo, Xizhao Department of Computer Science and Technology Soochow University Suzhou China Jiangsu Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou China

We construct a general Verifiable Fully Homomorphic Encryption, using the existed Fully Homomorphic Encryption schemes. The main appeal of the scheme is the verifiability of Evaluate function. Its security and other p... 详细信息

ISBN: (纸本)9781457714153

关键词： Cryptography

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