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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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Cone-Beam Computed Tomography Limited-Angle Image Reconstruction Using Planning CT-Guided Deep Diffusion Model

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IEEE Transactions on Instrumentation and Measurement 2025年

作者： Wu, Zhan Yang, Yang Hu, Dianlin Lyu, Tianling Li, Yinsheng Tang, Hao Shu, Huazhong Xi, Yan Zhang, Yi Chen, Yang Southeast University Laboratory of Image Science and Technology Nanjing 210096 China Southeast University Ministry of Education Key Laboratory of Computer Network and Information Integration Nanjing 210096 China Zhejiang University of Technology Academy for Advanced Interdisciplinary Science and Technology Hangzhou China Chinese Academy of Sciences Key Laboratory of Biomedical Imaging Science and System China Capital Medical University Beijing Jishuitan Hospital China First-Imaging Tech Shanghai China Sichuan University School of Cyber Science and Engineering Chengdu China Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Ministry of Education Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing Laboratory of Image Science and Technology the School of Computer Science and Engineering Nanjing 210096 China

For existing image-guided radiation therapy systems (IGRT), the limited angular range of the cone-beam computed tomography (CBCT) scanning improves the compactness of the system and compatibility between CBCT and linear accelerator configurations. However, the limited-angle CBCT reconstruction under incomplete projection data causes severe artifacts in the reconstructed images, further having a negative impact on the registration accuracy in IGRT. In this paper, we propose a planning CT-guided deep diffusion model, termed LARGE-DiffNet, to improve imaging quality and registration accuracy for limited-angle CBCT image reconstruction. In stark contrast to existing approaches leveraging hand-crafted regularization, such as local smoothness and/or sparsity of the image, LARGE-DiffNet learns the distribution-level prior knowledge to describe the features of ideal CBCT images via a planning CT-guided diffusion model. The proposed LARGE-DiffNet is evaluated on the clinical CBCT dataset, and extensive experimental results demonstrate that the proposed method outperforms the state-of-the-art methods. The proposed LARGE-DiffNet technique has great potential in IGRT. © 1963-2012 IEEE.

关键词： cone-beam computed tomography deep learning diffusion model image registration image-guided radiation therapy limited-angle

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Wireless Service Prediction Algorithm in Mobile Social Environment 2

Wireless Service Prediction Algorithm in Mobile Social Envir...

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2017 IEEE 2nd International Conference on Big Data Analysis（ICBDA 2017）

作者： Hui Zhang Min Wang Jiangsu Key Lab of Wireless Communications Nanjing University of Posts and Telecommunications Provincial Key Laboratory for Computer Information Processing Technology Soochow University

ISBN: (纸本)9781509036202

First,according to characteristics of mobile social environment,by using optimization models based on similarity degree and interaction degree respectively,the optimal correlated users can be selected for analyzing two main factors of a target user's behaviors(***-term habits and shortterm influences);furthermore,an adaptive update strategy based on fuzzy theory is proposed to describe the importance of two factors in real time and quantitative ***,an improved Apriori theory is introduced to predict user service behaviors accurately;particularly,a new update mechanism for Apriori sample database is built to effectively integrate the samples of optimal correlated ***,simulation results verify the effectiveness of proposed algorithm.

关键词： mobile social environment user behavior prediction fuzzy theory adaptive update strategy improved Apriori theory

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Neural Network-Assisted Hybrid Model Based Message Passing for Parametric Holographic MIMO Near Field Channel Estimation

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IEEE Transactions on Wireless Communications 2025年

作者： Yuan, Zhengdao Guo, Yabo Gao, Dawei Guo, Qinghua Wang, Zhongyong Huang, Chongwen Jin, Ming Wong, Kai-Kit Henan Open University Artificial Intelligence Technology Engineering Research Center Zhengzhou450002 China University of Wollongong School of Electrical Computer and Telecommunications Engineering WollongongNSW2522 Australia Zhengzhou University School of Information Engineering Zhengzhou450002 China Xidian University Hangzhou Institute of Technology Hangzhou311200 China University of Wollongong School of Electrical Computer and Telecommunications Engineering NSW2522 Australia Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China Zhejiang Provincial Key Laboratory of Multi-Modal Communication Networks and Intelligent Information Processing Hangzhou310027 China Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo315211 China University College London Department of Electronic and Electrical Engineering Torrington Place WC1E 7JE United Kingdom Yonsei University Yonsei Frontier Lab Seoul Korea Republic of

Holographic multiple-input and multiple-output (HMIMO) is a promising technology with the potential to achieve high energy and spectral efficiencies, enhance system capacity and diversity, etc. In this work, we address the challenge of HMIMO near field (NF) channel estimation, which is complicated by the intricate model introduced by the dyadic Green's function. Despite its complexity, the channel model is governed by a limited set of parameters. This makes parametric channel estimation highly attractive, offering substantial performance enhancements and enabling the extraction of valuable sensing parameters, such as user locations, which are particularly beneficial in mobile networks. However, the relationship between these parameters and channel gains is nonlinear and compounded by integration, making the estimation a formidable task. To tackle this problem, we propose a novel neural network (NN) assisted hybrid method. With the assistance of NNs, we first develop a novel hybrid channel model with a significantly simplified expression compared to the original one, thereby enabling parametric channel estimation. Using the readily availab.e training data derived from the original channel model, the NNs in the hybrid channel model can be effectively trained offline. Then, building upon this hybrid channel model, we formulate the parametric channel estimation problem with a probabilistic framework and design a factor graph representation for Bayesian estimation. Leveraging the factor graph representation and unitary approximate message passing (UAMP), we develop an effective message passing-based Bayesian channel estimation algorithm. Extensive simulations demonstrate the superior performance of the proposed method. © 2025 IEEE.

关键词： Green's function

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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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Context-Sensitive Convolution Tree Kernel for Pronoun Resolution 3

Context-Sensitive Convolution Tree Kernel for Pronoun Resolu...

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3rd International Joint Conference on Natural Language processing, IJCNLP 2008

作者： Zhou, GuoDong Kong, Fang Zhu, Qiaoming JiangSu Provincial Key Lab for Computer Information Processing Technology School of Computer Science and Technology Soochow Univ. Suzhou215006 China

This paper proposes a context-sensitive convolution tree kernel for pronoun resolution. It resolves two critical problems in previous researches in two ways. First, given a parse tree and a pair of an anaphor and an antecedent candidate, it implements a dynamic-expansion scheme to automatically determine a proper tree span for pronoun resolution by taking predicate- and antecedent competitor-related information into consideration. Second, it applies a context-sensitive convolution tree kernel, which enumerates both context-free and context-sensitive sub-trees by considering their ancestor node paths as their contexts. Evaluation on the ACE 2003 corpus shows that our dynamic-expansion tree span scheme can well cover necessary structured information in the parse tree for pronoun resolution and the context- sensitive tree kernel much outperforms previous tree kernels. © 2008 IJCNLP 2008 - 3rd International Joint Conference on Natural Language processing, Proceedings of the Conference. All rights reserved.

关键词： Convolution

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Semi-Supervised Learning for Relation Extraction 3

Semi-Supervised Learning for Relation Extraction

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3rd International Joint Conference on Natural Language processing, IJCNLP 2008

作者： Zhou, Guo Dong Li, Jun Hui Qian, Long Hua Zhu, Qiaoming Jiangsu Provincial Key Lab for Computer Information Processing Technology School of Computer Science and Technology Soochow Univ. Suzhou215006 China

This paper proposes a semi-supervised learning method for relation extraction. Given a small amount of lab.led data and a large amount of unlab.led data, it first bootstraps a moderate number of weighted support vectors via SVM through a co-training procedure with random feature projection and then applies a lab.l propagation (LP) algorithm via the bootstrapped support vectors. Evaluation on the ACE RDC 2003 corpus shows that our method outperforms the normal LP algorithm via all the availab.e lab.led data without SVM bootstrapping. Moreover, our method can largely reduce the computational burden. This suggests that our proposed method can integrate the advantages of both SVM bootstrapping and lab.l propagation.s. © 2008 IJCNLP 2008 - 3rd International Joint Conference on Natural Language processing, Proceedings of the Conference. All rights reserved.

关键词： Extraction

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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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The scientific literature corpus for Chinese negation and uncertainty identification

The scientific literature corpus for Chinese negation and un...

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14th Workshop on Chinese Lexical Semantics, CLSW 2013

作者： Chen, Zhancheng Zou, Bowei Zhu, Qiaoming Li, Peifeng School of Computer Science and Technology Soochow University Jiangsu 215006 China Key Lab. of Computer Information Processing Technology of Jiangsu Province Suzhou Jiangsu 215006 China

ISBN: (纸本)9783642451843

Negative and uncertain expressions widely exist in natural language. Negation and uncertainty identification has become an important task in computational linguistics community. Lacking of corpus hinders the development of Chinese negation and uncertainty identification. This paper reports on a corpus annotation project for Chinese negation and uncertainty identification on scientific literature. We introduce several important aspects during our annotation, such as selection of raw text, framework of annotation, guidelines, data consistency and statistic data summarized from corpus. In this corpus, we annotate negation and uncertainty cues and their corresponding scopes. Statistic for corpus shows that there are a certain proportion of negative and uncertain expressions in Chinese scientific literature. Accordingly, this corpus is able to support the research of Chinese negation and uncertainty identification in the future. © 2013 Springer-Verlag.

关键词： Computational linguistics

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Chinese zero anaphor detection: Rule-based approach

Chinese zero anaphor detection: Rule-based approach

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作者： Qin, Kaiwei Kong, Fang Li, Peifeng Zhu, Qiaoming School of Computer Science and Technology Soochow University Jiangsu 215006 China Key Lab. of Computer Information Processing Technology of Jiangsu Province Suzhou Jiangsu 215006 China

ISBN: (纸本)9783642256608

A rule-based approach for Chinese zero anaphor detection is proposed. Given a parse tree, the smallest IP sub-tree covering the current predicate is captured. Based on this IP sub-tree, some rules are proposed for detecting whether a Chinese zero anaphor exists. This paper also systematically evaluates the rule-based method on OntoNotes corpus. Using golden parse tree, our method achieves 82.45 in F-measure. And the F-measure is 63.84 using automatic parser. The experiment results show that our method is very effective on Chinese zero anaphor detection. © 2011 Springer-Verlag Berlin Heidelberg.

关键词： Forestry

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