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3rd CIPS-SIGHAN Joint Conference on Chinese Language processing, CLP 2014

作者： Yu, Junjie Li, Zhenghua Provincial Key Laboratory for Computer Information Processing Technology Soochow University China

Spelling check is an important preprocessing task when dealing with user generated texts such as tweets and product comments. Compared with some western languages such as English, Chinese spelling check is more complex because there is no word delimiter in Chinese written texts and misspelled characters can only be determined in word level. Our system works as follows. First, we use character-level n-gram language models to detect potential misspelled characters with low probabilities below some predefined threshold. Second, for each potential incorrect character, we generate a candidate set based on pronunciation and shape similarities. Third, we filter some candidate corrections if the candidate cannot form a legal word with its neighbors according to a word dictionary. Finally, we find the best candidate with highest language model probability. If the probability is higher than a predefined threshold, then we replace the original character;or we consider the original character as correct and take no action. Our preliminary experiments shows that our simple method can achieve relatively high precision but low recall. © 2014 CLP 2014 - 3rd CIPS-SIGHAN Joint Conference on Chinese Language processing. All rights reserved.

关键词： Probability

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VLSI multilayer obstacle-avoiding Steiner minimal tree construction algorithms in X-architecture

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Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao/Journal of computer-Aided Design and computer Graphics 2015年第3期27卷 523-532页

作者： Liu, Genggeng Guo, Wenzhong Chen, Guolong College of Mathematics and Computer Sciences Fuzhou University Fuzhou350116 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou350116 China

Steiner minimal tree is a fundamental model in VLSI routing. Further considering X-architecture, the presence of obstacles, and the requirements of multilayer routing, this paper presented an efficient algorithm based on particle swarm optimization for constructing the multilayer obstacle-avoiding X-architecture Steiner minimal tree. Edge transformation was designed to make the particles have the greater ability to change the topology of the routing tree and avoided the obstacles. The new operators combined with union-find set were designed to prevent the generation of the loops. In order to avoid obstacle and discourage the generation of vias, the penalty function related to obstacle-avoiding and vias was proposed. The proposed algorithm finally optimizes the total cost of routing tree which is the most important optimization target of the multilayer routing. The experimental results show that, the proposed algorithm is effective and superior to state-of-the-art multilayer routing algorithms on the total cost. ©, 2015, Institute of Computing technology. All right reserved.

关键词： Multilayers

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Soft cross-Lingual syntax projection for dependency parsing 25

Soft cross-Lingual syntax projection for dependency parsing

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25th International Conference on Computational Linguistics, COLING 2014

作者： Li, Zhenghua Zhang, Min Chen, Wenliang Provincial Key Laboratory for Computer Information Processing Technology Soochow University China

ISBN: (纸本)9781941643266

This paper proposes a simple yet effective framework of soft cross-lingual syntax projection to transfer syntactic structures from source language to target language using monolingual treebanks and large-scale bilingual parallel text. Here, soft means that we only project reliable dependencies to compose high-quality target structures. The projected instances are then used as additional training data to improve the performance of supervised parsers. The major issues for this idea are 1) errors from the source-language parser and unsupervised word aligner;2) intrinsic syntactic non-isomorphism between languages;3) incomplete parse trees after projection. To handle the first two issues, we propose to use a probabilistic dependency parser trained on the target-language treebank, and prune out unlikely projected dependencies that have low marginal probabilities. To make use of the incomplete projected syntactic structures, we adopt a new learning technique based on ambiguous labelings. For a word that has no head words after projection, we enrich the projected structure with all other words as its candidate heads as long as the newly-added dependency does not cross any projected dependencies. In this way, the syntactic structure of a sentence becomes a parse forest (ambiguous labels) instead of a single parse tree. During training, the objective is to maximize the mixed likelihood of manually labeled instances and projected instances with ambiguous labelings. Experimental results on benchmark data show that our method significantly outperforms a strong baseline supervised parser and previous syntax projection methods.

关键词： Syntactics

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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 deep learning for natural language processing

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Zidonghua Xuebao/Acta Automatica Sinica 2016年第10期42卷 1445-1465页

作者： Xi, Xue-Feng Zhou, Guo-Dong School of Computer Science and Technology Soochow University Suzhou215006 China School of Electronic and Information Engineering Suzhou University of Science and Technology Suzhou215009 China Suzhou Key Laboratory of Mobile Networking and Applied Technologies Suzhou215009 China

Recently, deep learning has made significant development in the fields of image and voice processing. However, there is no major breakthrough in natural language processing task which belongs to the same category of human cognition. In this paper, firstly the basic concepts of deep learning are introduced, such as application motivation, primary task and basic framework. Secondly, in terms of both data representation and learning model, this paper focuses on the current research progress and application strategies of deep learning for natural language processing, and further describes the current deep learning platforms and tools. Finally, the future development difficulties and suggestions for possible extensions are also discussed. Copyright © 2016 Acta Automatica Sinica. All rights reserved.

关键词： Natural language processing systems

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An evolution-based robust social influence evaluation method in Online Social Networks

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2014年 8787卷 141-157页

作者： Zhu, Feng Liu, Guanfeng Liu, An Zhao, Lei Zhou, Xiaofang School of Computer Science and Technology Jiangsu Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou215006 China School of Information Technology and Electrical Engineering University of Queensland Brisbane4072 Australia

Online Social Networks (OSNs) are becoming popular and attracting lots of participants. In OSN based e-commerce platforms, a buyer’s review of a product is one of the most important factors for other buyers’ decision makings. A buyer who provides high quality reviews thus has strong social influence, and can impact a large number of participants’ purchase behaviours in OSNs. However, the dishonest participants can cheat the existing social influence evaluation models by using some typical attacks, like Constant and Camouflage, to obtain fake strong social influence. Therefore, it is significant to accurately evaluate such social influence to recommend the participants who have strong social influences and provide high quality product reviews. In this paper, we propose an Evolutionary-Based Robust Social Influence (EB-RSI) method based on the trust evolutionary models. In our EB-RSI, we propose four influence impact factors in social influence evaluation, i.e., Total Trustworthiness (TT), Fluctuant Trend of Being Advisor (FTBA), Fluctuant Trend of Trustworthiness (FTT) and Trustworthiness Area (TA). They are all significant in the influence evaluation. We conduct experiments onto a real social network dataset Epinions, and validate the effectiveness and robustness of our EB-RSI by comparing with state-of-the-art method, SoCap. The experimental results demonstrate that our EB-RSI can more accurately evaluate participants’ social influence than SoCap. © Springer International Publishing Switzerland 2014.

关键词： Social networking (online)

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Building cognizance rule knowledge for fault diagnosis based on fuzzy rough sets

Building cognizance rule knowledge for fault diagnosis based...

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作者： He, Xi-Xu Chen, Lei-Ting Jia, Hai-Tao School of Computer Science and Engineering University of Electronic Science and Technology of China Sichuan China Dongguan Institute of Information Engineering University of Electronic Science and Technology of China Sichuan China Center of Information University of Electronic Science and Technology of China Chengdu Sichuan China Provincial Key Laboratory of Digital Media Chengdu Sichuan China

With the continuous development of huge systems, dependence on the system is continually increasing. The failure of such systems will cause huge losses. The reason for system failure is often unclear, so that inconsistency and uncertainty between fault data will appear. In the actual application process, there is a process of change. If it is possible to predict the failure probability from the monitoring parameters, it will be very beneficial to system troubleshooting. Therefore, this paper proposes a new recognition algorithm based on fuzzy rough sets, in order to adapt to the processing of uncertain fault detection data. Additionally, the optimal direction of the dynamic information entropy increment is used to predict the fault information. This can quickly find the faults and provide important information for fault detection. It is verified that the proposed algorithm can improve the early warning and the accuracy of fault diagnosis information systems in the fault simulation analysis of a diesel engine. © 2015 - IOS Press and the authors. All rights reserved.

关键词： Failure analysis

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The Recognition of Maize seeds Based on Multi-scale Feature Fusion and Extreme Learning Machine

The Recognition of Maize seeds Based on Multi-scale Feature ...

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2015 3rd International Conference on Mechanical Engineering and Intelligent Systems(ICMEIS 2015)

作者： Mingzhi Du Xiao Ke Mingke Zhou College of Mathematics and Computer Science Fuzhou University Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing (Fuzhou University)

In recognizing traditional crops seeds like maize seeds, we usually use electrophoresis assay method, fluorescence scanning method and chemical assay method. These methods are destructive methods. They take a long time to detect and are demanding of professional background knowledge and hardware conditions etc. What's more, these methods, based on BP neural network and support vector machine(SVM)while taking a long time to detect are less accurate in process of classification. In this paper, based on the computer vision technology, we proposed a new method for the classification of maize seeds, a method based on multi-scale feature fusion and extreme learning machine. First, we extract the multi-scale fusion feature of maize seeds. Second, based on extreme learning machine, we construct the classifier model of maize seed. Third, because of the window of image in the case of multi-scale detection has the problem of capturing the same object seed with many overlapping windows, we put forward a kind of window fusion algorithm to solve it. The simulation results show that: The method is able to identify the maize seeds accurately. Using this method the accuracy of classification of maize seeds can reached 97.66% and the error rate is less than 0.1%. Compared with the traditional methods, the method we proposed can improve the speed of detection and the accuracy of classification, and has no strict hardware requirements.

关键词： extreme learning machine feature fusion object recognition

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Vector Localization Algorithm Based on Signal Strength in Wireless Sensor Network

Vector Localization Algorithm Based on Signal Strength in Wi...

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International Symposium on Parallel Architectures, Algorithms and Programming (PAAP)

作者： Jun Xu Min Wu Chao Sha Tianyu Lu Ruchuan Wang Computer and Software College of Nanjing University of Posts and Telecommunications NanJing Provincial Kev Laboratory for Computer Information Processing Technology Soochow University SuZhou School of Engineering and Computation New York Institute of Technology New York NY USA

To deal with the inaccuracy and the uniform distribution of the DV-Hop algorithm, it is necessary to put forward a new optimization algorithm based on DV-Hop and RSSI for the two-dimensional planar node location called RSDV-Hop. The algorithm is based on the shortest hop distance with RSSI algorithm and gets the position between the nodes and the anchors according to the difference of signal strength. Moreover, it estimate the distance between the nodes and the anchors on the basis of the difference of signal strength. Therefore it can reduce the influence of the error of signal strength and the distance as far as possible by choosing different localization algorithm based on the different relationship of the nodes and the anchors. Experimental results show that the proposed algorithm performance well on the positioning accuracy and have better success rate than the DV-Hop and WPDV-Hop.

关键词： Attenuation Algorithm design and analysis Classification algorithms Fitting Distance measurement Software algorithms Wireless sensor networks

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Local Structure Co-occurrence Pattern for Texture Image Retrieval

Local Structure Co-occurrence Pattern for Texture Image Retr...

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2015 International Conference on Simulation, Modelling and Mathematical Statistics(SMMS 2015)

作者： Ke ZHANG Fan ZHANG Jia LU Ying-hua LU Jun KONG Ming ZHANG School of Computer Science and Information Technology Northeast Normal University Key Laboratory of Intelligent Information Processing Northeast Normal University

In this paper, an image feature descriptor called Local structure co-occurrence pattern(LSCP) is proposed. LSCP characterizes image structure via building local binary structure(LBS) based on local binary pattern(LBP), and is represented by co-occurrence matrix according to visual attention mechanism. Therefore, LSCP not only describes low-level visual features integrated with texture feature, color feature and shape feature, but also bridges high-level semantic comprehension. When applied to content-based texture image retrieval and compared with other existing methods on the benchmark datasets, MIT Vis Tex(40 textures), the usefulness, effectiveness and robustness of LSCP are obvious.

关键词： Local structure co-occurrence pattern Semantic comprehension Content-Based texture image retrieval

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