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检索条件"机构=AnHui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine HeFei AnHui"

共 114 条记录，以下是61-70 订阅

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Semantic polarity detection of Chinese multiword expression in microblogging based on discriminative latent model

Semantic polarity detection of Chinese multiword expression ...

引用

作者： Sun, Xiao Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer and Information Hefei University of Technology Hefei230009 China

Extracting semantic polarity of Chinese Multiword Expression, especially some newly generated Multiword Expression from internet(such as weibo or microblog), is an important task for sentiment analysis of web texts or other real word text as some Multiword Expressions can express more integrative sentiments than words units. This paper proposes a method that contains a novel latent discriminative algorithm, which attempts to attack this problem by integrating discriminative model and latent value model. Although Chinese Multiword Expressions consist of multiple words, the semantic polarity of the Multiword Expression is not just simple integration of polarities of the component words, as some words can invert the affective polarity so the Multiword Expressions can have totally opposite semantic polarity, such as ironic texts. In order to capture the property of such Multiword Expressions, hidden semi-CRF which includes a latent valuable layer, which can be used to address dual-sequence labeling tasks synchronously, is adopted. The method is tested experimentally by adopting a manually labeled set of positive and negative Multiword Expressions from microblog or other internet resources, and the experiments have shown very promising results, which is comparable to the best value ever reported. © 2014-IOS Press and the authors. All rights reserved.

关键词： Sentiment analysis

来源：评论

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Chinese microblogging emotion classification based on support vector machine 5

Chinese microblogging emotion classification based on suppor...

引用

5th International Conference on computing Communication and Networking Technologies, ICCCNT 2014

作者： Sun, Xiao Li, Chengcheng Ye, Jiaqi Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer and Information Hefei University of Technology Hefei230009 China

ISBN: (纸本)9781479926961

Through the analysis and study of the emotional characteristics in Chinese micro-blog, this paper proposed a multidimensional sentiment classification method based micro-blog emotion classification, emoticon in micro-blog is adopted to train the support vector machine and divide micro-blog into seven types of emotions: happiness, fondness, sorrow, anger, fear, detestation and surprise. We used micro-blog emoticon to initial screen large-scale unmarked data, and automatically labeled them into seven types, then used this emotional corpus as training set to train the emotion classifier to classify micro-blog data into multiple emotion categories. The experimental results show that precision of unigram model for each type can reach 63.7%. Meanwhile, different feature selection methods for support vector machines and Naive Bayes classifier experiment have been adopted in the experiment, the precision and recall has reached more than 71%. © 2014 IEEE.

关键词： Support vector machines

来源：评论

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Real Time Early-stage Influenza Detection with Emotion Factors from Sina Microblog 5

Real Time Early-stage Influenza Detection with Emotion Facto...

引用

5th Workshop on South and Southeast Asian NLP, WSSANLP 2014

作者： Sun, Xiao Ye, Jiaqi Ren, Fuji School of Computer and Information Hefei University of Technology Anhui Hefei China Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine China Faculty of Engineering University of Tokushima Tokushima Japan

ISBN: (纸本)9781873769416

Influenza is an acute respiratory illness that occurs every year. Detection of Influenza in its earliest stage would reduce the spread of the illness. Sina microblog is a popular microblogging service, provides perfect sources for flu detection due to its real-time nature and large number of users. In this paper we investigate the real-time flu detection problem and describe a Flu model with emotion factors and sematic information (em-flu model). Experimental results show the robustness and effectiveness of our method and we are hopeful that it would help health organizations in identifying flu outbreak and take timely actions to control. © 2014 Proceedings of the Conference - 5th Workshop on South and Southeast Asian NLP, WSSANLP 2014 - co-located with the 25th International Conference on Computational Linguistics, COLING 2014. All rights reserved.

关键词： Diseases

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Gene-disease association extraction by text mining and network analysis 5

Gene-disease association extraction by text mining and netwo...

引用

5th International Workshop on Health Text Mining and Information Analysis, Louhi 2014 at the 14th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2014

作者： Quan, Changqin Ren, Fuji AnHui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer and Information HeFei University of Technology China Faculty of Engineering University of Tokushima Japan

ISBN: (纸本)9781937284909

Biomedical relations play an important role in biological processes. In this work, we combine information filtering, grammar parsing and network analysis for gene-disease association extraction. The proposed method first extracts sentences potentially containing information about gene-diseases interactions based on maximum entropy classifier with topic features. And then Probabilistic Context-Free Grammars is applied for gene-disease association extraction. The network of genes and the disease is constituted by the extracted interactions, network centrality metrics are used for calculating the importance of each gene. We used breast cancer as testing disease for system evaluation. The 31 top ranked genes and diseases by the weighted degree, betweenness, and closeness centralities have been checked relevance with breast cancer through NCBI database. The evaluation showed 83.9% accuracy for the testing genes and diseases, 74.2% accuracy for the testing genes. © 2014 Association for Computational Linguistics

关键词： Genes

来源：评论

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Expression image preprocessing based on wavelet transform

Expression image preprocessing based on wavelet transform

引用

2014 IEEE International Conference on Robotics and Biomimetics, IEEE ROBIO 2014

作者： Quan, Changqin Xu, Xiaoming Ren, Fuji Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer and Information Hefei University of Technology Hefei230009 China University of Tokushima Shinkura-cho Tokushima770-8501 Japan

ISBN: (纸本)9781479973965

In this paper, we present a new image preprocessing method which is based on Wavelet Transform. Before we extract expression texture information, Wavelet transform will be applied to remove partial high frequency information. And then Gabor Transform will be used to extract expression information. At last we chose the Support Vector machine to classify facial expressions. Experiments on the Japanese Female Facial Expression (JAFFE) Database illustrate that the Wavelet Transform and Gabor features are effective for facial expression discrimination. Additionally, the experiment results validate the feasibility of this method. © 2014 IEEE.

关键词： Wavelet transforms

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A Connected Component-Based Distributed method for overlapping community detection 5

A Connected Component-Based Distributed method for overlappi...

引用

5th International Conference on computing Communication and Networking Technologies, ICCCNT 2014

作者： Chen, Tian Liu, Kai Yi, Xin Shen, Dandan Wang, Wei Ren, Fuji Liu, Jun School of Computer and Information Hefei University of Technology Hefei230009 China Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine Hefei University of Technology Hefei230009 China AnHui Agricultural University Hefei230036 China University of Tokushima 770-8506 Japan

ISBN: (纸本)9781479926961

In order to find the overlapping community structure more quickly and accurately in complex network, this paper proposes a Connected Component-Based Distributed method (CCBD) for overlapping community detection. CCBD first divides edges set in the network into smaller one. Next, it seeks out connected component using distributed platforms and gives a serials number to them according to certain rules. Then, it classifies these connected components according to the serials number. Task nodes determine whether any two connected components and non-propagating edges can be connected to become a larger community. Finally, CCBD negotiates a new serials name for the new community. Through repeated iterations, we get the community structure in the network. Nodes belong to multiple communities are overlapping nodes. Experiment results show that CCBD has higher time efficiency benefiting from distributed computing. Moreover, the quality of communities detected by CCBD surpasses those found by other algorithms. © 2014 IEEE.

关键词： Complex networks

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A low power BIST scheme based on block encoding 5

A low power BIST scheme based on block encoding

引用

5th International Conference on computing Communication and Networking Technologies, ICCCNT 2014

作者： Chen, Tian Zheng, Liuyang Wang, Wei Ren, Fuji Zhang, Xishan Chang, Hao School of Computer and Information Hefei University of Technology Hefei230009 China AnHui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine Hefei University of Technology Hefei230009 China University of Tokushima 770-8506 Japan

ISBN: (纸本)9781479926961

With the development of integrated circuit manufacturing technology, low power test has become a focus of concern during testing fields. This paper proposes a new low power BIST (built-in self test) scheme based on block encoding which first exploit a block re-encoding method to optimize the test cube, and then a low power test based on LFSR (linear feedback shift register) reseeding is applied. According to the compatibility of flag, the scheme proposes a grouping algorithm based on flag to divide and reorder the test cubes in the test cube set. Experimental results show that the scheme not only obtain better test compression ratio and test data storage, but also reduce the test power consumption effectively. © 2014 IEEE.

关键词： Shift registers

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A test data compression scheme based on position information coding 5

A test data compression scheme based on position information...

引用

5th International Conference on computing Communication and Networking Technologies, ICCCNT 2014

作者： Chen, Tian Yang, Bingdong Wang, Wei Hao, Birong Ren, Fuji Liu, Jun Wang, Yixin School of Computer and Information Hefei University of Technology Hefei230009 China AnHui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine Hefei University of Technology Hefei230009 China University of Tokushima 770-8506 Japan

ISBN: (纸本)9781479926961

Under the condition of Built-off Self-test (BOST), a novel test data compression scheme based on position information coding is presented, which is targeting test data and test power consumption. The proposed scheme gets the next test stimuli by reversing some bits of the responses corresponding to the previous test vectors. During the process of bit flip, test power problem should be taken into account;both the shift power and capture power during the test should be limited in the scope of security. For shift power, need to modify the scan chain by adding blocking logic;and the capture power reduction through the test cube filling based on the response. Experimental results justify the efficacy of the proposed method in attaining test-data compression;the average compression ratio is 73.63% on the premise of capture power consumption reduced by 20%. © 2014 IEEE.

关键词： Electric power utilization

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Test wrapper optimization technique using BDF and GA for 3D IP cores 5

Test wrapper optimization technique using BDF and GA for 3D ...

引用

5th International Conference on computing Communication and Networking Technologies, ICCCNT 2014

作者： Liu, Jun Qian, Qingqing Wu, Xi Ren, Fuji Wang, Wei Chen, Tian School of Computer and Information Hefei University of Technology Hefei230009 China AnHui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine Hefei University of Technology Hefei230009 China Department of Information Science and Intelligent Systems Faculty of Engineering University of Tokushima Tokushima7708502 Japan

ISBN: (纸本)9781479926961

To reduce pre-bond and post-bond test cost for 3D IP (Three Dimensional Intellectual Property) cores, this paper proposed a test wrapper optimization technique using BFD(Best Fit Decreasing) and GA (Genetic Algorithm) algorithm under the constraints of TSVs(Through Silicon Vias) number, the proposed technique firstly used BFD to balance the length of pre-bond wrapper chains to reduce pre-bond test time. Then, on the basis of optimization results of pre-bond wrapper chains, the GA was used to stitch pre-bond wrapper chains to form balanced post-bond wrapper chains under the constrained TSVs number to reduce hardware overhead and post-bond test time. Experimental results demonstrated the presented methodology can effectively reduce hardware overhead at the cost of little increased test time. © 2014 IEEE.

关键词： Chains

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Hybrid model based sentiment classification of Chinese micro-blog

Hybrid model based sentiment classification of Chinese micro...

引用

International Conference on Audio, Language and Image Processing, ICALIP

作者： Xiao Sun Chengcheng Li Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine Hefei University of Technology Hefei China

ISBN: (纸本)9781479939046

Through analysis and study of emotional characteristics in Chinese micro-blog, such as Sina Weibo, this paper proposed a multidimensional sentiment classification method based on micro-blog emoticon by dividing micro-blog into 7 types of emotions categories: happiness, fondness, sorrow, anger, fear, detestation and surprise. We used predefined micro-blog emoticon sets to initial screen large-scale unmarked data, and automatically labeled them, then used this emotional corpus as training set to train the emotion classifier, which divided micro-blog data into multiple emotion categories. The experimental results show that accuracy rate of using unigram model for each class can reach 63.7%. And the adoption of different feature selection methods for Support Vector machines and Naive Bias classifier experiment, by which the obtained accuracy rate and recall rate has reached higher than 71%.

关键词： Accuracy Blogs Support vector machines Twitter Niobium Educational institutions Training

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