Research on Contrastive Viewpoint Summarization for Opinionated Texts

2013 ◽  
Vol 14 (03) ◽  
pp. 1360003 ◽  
Author(s):  
XU LIANG ◽  
YOULI QU ◽  
GUIXIANG MA
Keyword(s):  
Experimental Results ◽  
Two Stage ◽  
Second Stage

This paper presents a two-stage approach for multi-topic contrastive viewpoint summarization on opinionated texts. In the first stage, we model the opinionated texts with TAM and get the topic and aspect attribute of each sentence. In the second stage, we successively use the basic LexRank, Comparative LexRank, Topic-sensitive tf*idf LexRank, Topic-sensitive tf*idf & Comparative LexRank, and Biased & Comparative LexRank to evaluate the centrality of each sentence, based on which the summary is finally generated. Experimental results show that the best summary comes from the proposed Topic-sensitive tf*idf LexRank and Topic-sensitive tf*idf & Comparative LexRank.

scholarly journals Efficacy-specific Herbal Group Detection from Traditional Chinese Medicine Prescriptions via Hierarchical Attentive Neural Network Model

2020 ◽  
Author(s):  
Li Chen ◽  
Xinglong Liu ◽  
Siyuan Zhang ◽  
Hong Yi ◽  
Yongmei Lu ◽  
...  
Keyword(s):  
Neural Network ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Network Model ◽  
Neural Network Model ◽  
Experimental Results ◽  
Frequent Patterns ◽  
Two Stage ◽  
Second Stage ◽  
Group Detection

Abstract Background: Mining massive prescriptions in Traditional Chinese Medicine (TCM) accumulated in the lengthy period of several thousand years to discover essential herbal groups for distinct efficacies is of significance for TCM modernization, thus starting to draw attentions recently. However, most existing methods for the task treat herbs with different surface forms orthogonally and determine efficacy-specific herbal groups based on the raw frequencies an herbal group occur in a collection of prescriptions. Such methods entirely overlook the fact that prescriptions in TCM are formed empirically by different people at different historical stages, and thus full of herbs with different surface forms expressing the same material, or even noisy and redundant herbs.Methods: We propose a two-stage approach for efficacy-specific herbal group detection from prescriptions in TCM. For the first stage we devise a hierarchical attentive neural network model to capture essential herbs in a prescription for its efficacy, where herbs are encoded with dense real-valued vectors learned automatically to identify their differences on the semantical level. For the second stage, frequent patterns are mined to discover essential herbal groups for an efficacy from distilled prescriptions obtained in the first stage.Results: We verify the effectiveness of our proposed approach from two aspects, the first one is the ability of the hierarchical attentive neural network model to distill a prescription, and the second one is the accuracy in discovering efficacy-specific herbal groups.Conclusion: The experimental results demonstrate that the hierarchical attentive neural network model is capable to capture herbs in a prescription essential to its efficacy, and the distilled prescriptions significantly could improve the performance of efficacy-specific herbal group detection.

A Novel Initial Alignment Scheme for Low-Cost INS Aided by GPS for Land Vehicle Applications

2010 ◽  
Vol 63 (4) ◽  
pp. 663-680 ◽  
Author(s):  
Songlai Han ◽  
Jinling Wang
Keyword(s):  
Kalman Filtering ◽  
Inertial Sensors ◽  
Low Cost ◽  
Experimental Results ◽  
Initial Alignment ◽  
Two Stage ◽  
Second Stage ◽  
Attitude Error ◽  
Error Dynamic ◽  
Error Dynamics

This paper proposes a novel mechanism for the initial alignment of low-cost INS aided by GPS. For low-cost INS, the initial alignment is still a challenging issue because of the high noises from low-cost inertial sensors. In this paper, a two-stage Kalman Filtering mechanism is proposed for the initial alignment of low-cost INS. The first stage is designed for the coarse alignment. To solve the problems encountered by the general coarse alignment approach, an INS error dynamic accounting for unknown initial heading error is developed, and the corresponding observation equation, taking into account the unknown heading error, is also developed. The second stage is designed for the fine alignment, where the classical INS error dynamics based on small attitude error is used. Experimental results indicate that the proposed alignment approach can complete the initial alignment more quickly and more accurately compared with the conventional approach.

scholarly journals Efficacy-specific Herbal Group Detection from Traditional Chinese Medicine Prescriptions via Hierarchical Attentive Neural Network Model

2020 ◽  
Author(s):  
li Chen ◽  
Xinglong Liu ◽  
Siyuan Zhang ◽  
Hong Yi ◽  
Yongmei Lu ◽  
...  
Keyword(s):  
Neural Network ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Network Model ◽  
Neural Network Model ◽  
Experimental Results ◽  
Frequent Patterns ◽  
Two Stage ◽  
Second Stage ◽  
Group Detection

Abstract Background: Mining massive prescriptions in Traditional Chinese Medicine (TCM) accumulated in the lengthy period of several thousand years to discover essential herbal groups for distinct efficacies is of significance for TCM modernization, thus starting to draw attentions recently. However, most existing methods for the task treat herbs with different surface forms orthogonally and determine efficacy-specific herbal groups based on the raw frequencies an herbal group occur in a collection of prescriptions. Such methods entirely overlook the fact that prescriptions in TCM are formed empirically by different people at different historical stages, and thus full of herbs with different surface forms expressing the same material, or even noisy and redundant herbs. Methods: We propose a two-stage approach for efficacy-specific herbal group detection from prescriptions in TCM. For the first stage we devise a hierarchical attentive neural network model to capture essential herbs in a prescription for its efficacy, where herbs are encoded with dense real-valued vectors learned automatically to identify their differences on the semantical level. For the second stage, frequent patterns are mined to discover essential herbal groups for an efficacy from distilled prescriptions obtained in the first stage. Results: We verify the effectiveness of our proposed approach from two aspects, the first one is the ability of the hierarchical attentive neural network model to distill a prescription, and the second one is the accuracy in discovering efficacy-specific herbal groups. Conclusion: The experimental results demonstrate that the hierarchical attentive neural network model is capable to capture herbs in a prescription essential to its efficacy, and the distilled prescriptions significantly could improve the performance of efficacy-specific herbal group detection.

Modeling and Dynamic Evaluation of a Two-Stage Two-Spool Servovalve Used for Pressure Control

1991 ◽  
Vol 113 (4) ◽  
pp. 709-713 ◽  
Author(s):  
S. T. Tsai ◽  
A. Akers ◽  
S. J. Lin
Keyword(s):  
Mathematical Model ◽  
Control Valve ◽  
Pressure Control ◽  
Experimental Results ◽  
Two Stage ◽  
Dynamic Evaluation ◽  
Second Stage ◽  
Experimental Values ◽  
Pressure Control Valve ◽  
Good Agreement

Experimental results for a unique design of a two-spool pressure control valve were reported by Anderson (1984). The first stage is a dynamically stable flapper-nozzle valve for which a mathematical model is already available (Lin and Akers, 1989a). For the second stage, however, which consists of two parallel spools in a common body, no such model existed. The purpose of this paper was therefore to construct such a model and to compare results calculated from it to experimental values. Moderately good agreement with experimental values was obtained.

scholarly journals Efficacy-specific Herbal Group Detection from Traditional Chinese Medicine Prescriptions via Hierarchical Attentive Neural Network Model

2021 ◽  
Author(s):  
Li Chen ◽  
Xinglong Liu ◽  
Siyuan Zhang ◽  
Hong Yi ◽  
Yongmei Lu ◽  
...  
Keyword(s):  
Neural Network ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Network Model ◽  
Neural Network Model ◽  
Experimental Results ◽  
Frequent Patterns ◽  
Two Stage ◽  
Second Stage ◽  
Group Detection

Abstract Background: Mining massive prescriptions in Traditional Chinese Medicine (TCM) accumulated in the lengthy period of several thousand years to discover essential herbal groups for distinct efficacies is of significance for TCM modernization, thus starting to draw attentions recently. However, most existing methods for the task treat herbs with different surface forms orthogonally and determine efficacy-specific herbal groups based on the raw frequencies an herbal group occur in a collection of prescriptions. Such methods entirely overlook the fact that prescriptions in TCM are formed empirically by different people at different historical stages, and thus full of herbs with different surface forms expressing the same material, or even noisy and redundant herbs.Methods: We propose a two-stage approach for efficacy-specific herbal group detection from prescriptions in TCM. For the first stage we devise a hierarchical attentive neural network model to capture essential herbs in a prescription for its efficacy, where herbs are encoded with dense real-valued vectors learned automatically to identify their differences on the semantical level. For the second stage, frequent patterns are mined to discover essential herbal groups for an efficacy from distilled prescriptions obtained in the first stage.Results: We verify the effectiveness of our proposed approach from two aspects, the first one is the ability of the hierarchical attentive neural network model to distill a prescription, and the second one is the accuracy in discovering efficacy-specific herbal groups.Conclusion: The experimental results demonstrate that the hierarchical attentive neural network model is capable to capture herbs in a prescription essential to its efficacy, and the distilled prescriptions significantly could improve the performance of efficacy-specific herbal group detection.

Recovery and encapsulation of heavy metals on refinery spent hydrotreating catalyst

2000 ◽  
Vol 42 (5-6) ◽  
pp. 71-77 ◽  
Author(s):  
D.D. Sun ◽  
J.H. Tay ◽  
C. Easton
Keyword(s):  
Heavy Metals ◽  
Sulfuric Acid ◽  
Effective Means ◽  
Experimental Results ◽  
Leaching Test ◽  
Marine Clay ◽  
Spent Catalyst ◽  
Two Stage ◽  
Second Stage ◽  
Hydrotreating Catalyst

The experimental results indicate that spent hydrotreating catalyst (Co/Mo/γAl2O3) contains carbon (C), sulfur (S), molybdenum (Mo), cobalt (CO) and vanadium (V) at levels of 16 wt.%, 7.3 wt.%, 10.9 wt.%, 4.0 wt.% and 4.6 wt.% respectively. Calcination at 500°C is an effective process for the removal of carbon and sulfur and generates oxide form of heavy metals. Optimum removal of these metals was achieved by two stage leaching process. The first stage using concentrated ammonia was successful in removing 83 w/v % Mo. In a second stage with 10% v/v% sulfuric acid, it was found that 77% w/v% Co together with 4 w/v% Mo were removed from the spent Co/Mo/γAl2O3. A TCLP leaching test confirmed that leaching treatment produced residue that was almost stabilised with respect to V but not for Co and Mo. Enhanced binder stabilization treatment generated a commercial value brick, with up to 30 wt.% spent catalyst in marine clay which proved to be an effective means of heavy metals stabilization.

scholarly journals Efficacy-specific herbal group detection from traditional Chinese medicine prescriptions via hierarchical attentive neural network model

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Li Chen ◽  
Xinglong Liu ◽  
Siyuan Zhang ◽  
Hong Yi ◽  
Yongmei Lu ◽  
...  
Keyword(s):  
Neural Network ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Network Model ◽  
Neural Network Model ◽  
Experimental Results ◽  
Frequent Patterns ◽  
Two Stage ◽  
Second Stage ◽  
Group Detection

Abstract Background Mining massive prescriptions in Traditional Chinese Medicine (TCM) accumulated in the lengthy period of several thousand years to discover essential herbal groups for distinct efficacies is of significance for TCM modernization, thus starting to draw attentions recently. However, most existing methods for the task treat herbs with different surface forms orthogonally and determine efficacy-specific herbal groups based on the raw frequencies an herbal group occur in a collection of prescriptions. Such methods entirely overlook the fact that prescriptions in TCM are formed empirically by different people at different historical stages, and thus full of herbs with different surface forms expressing the same material, or even noisy and redundant herbs. Methods We propose a two-stage approach for efficacy-specific herbal group detection from prescriptions in TCM. For the first stage we devise a hierarchical attentive neural network model to capture essential herbs in a prescription for its efficacy, where herbs are encoded with dense real-valued vectors learned automatically to identify their differences on the semantical level. For the second stage, frequent patterns are mined to discover essential herbal groups for an efficacy from distilled prescriptions obtained in the first stage. Results We verify the effectiveness of our proposed approach from two aspects, the first one is the ability of the hierarchical attentive neural network model to distill a prescription, and the second one is the accuracy in discovering efficacy-specific herbal groups. Conclusion The experimental results demonstrate that the hierarchical attentive neural network model is capable to capture herbs in a prescription essential to its efficacy, and the distilled prescriptions significantly could improve the performance of efficacy-specific herbal group detection.

scholarly journals Multimodality Data Analysis in Information Security ETCC: Encrypted Two-Label Classification Using CNN

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yan Li ◽  
Yifei Lu
Keyword(s):  
Data Analysis ◽  
Information Security ◽  
Experimental Results ◽  
Two Stage ◽  
Second Stage ◽  
Public Dataset ◽  
Encrypted Traffic ◽  
Application Classification ◽  
Refined Classification

Due to the increasing variety of encryption protocols and services in the network, the characteristics of the application are very different under different protocols. However, there are very few existing studies on encrypted application classification considering the type of encryption protocols. In order to achieve the refined classification of encrypted applications, this paper proposes an Encrypted Two-Label Classification using CNN (ETCC) method, which can identify both the protocols and the applications. ETCC is a two-stage two-label classification method. The first stage classifies the protocol used for encrypted traffic. The second stage uses the corresponding classifier to classify applications according to the protocol used by the traffic. Experimental results show that the ETCC achieves 97.65% accuracy on a public dataset (CICDarknet2020).

An Enhanced Two Stage MMSE Equalizer for Coded FBMC/OQAM Systems

2019 ◽  
Vol 10 (1) ◽  
pp. 69-82 ◽  
Author(s):  
Mohammad Rizk Assaf ◽  
Abdel-Nasser Assimi
Keyword(s):  
Error Propagation ◽  
Channel Model ◽  
Error Correcting Codes ◽  
Two Stage ◽  
Probability Of Error ◽  
Second Stage ◽  
Different Types ◽  
Mmse Equalizer ◽  
And Performance ◽  
Ici Cancellation

In this article, the authors investigate the enhanced two stage MMSE (TS-MMSE) equalizer in bit-interleaved coded FBMC/OQAM system which gives a tradeoff between complexity and performance, since error correcting codes limits error propagation, so this allows the equalizer to remove not only ICI but also ISI in the second stage. The proposed equalizer has shown less design complexity compared to the other MMSE equalizers. The obtained results show that the probability of error is improved where SNR gain reaches 2 dB measured at BER compared with ICI cancellation for different types of modulation schemes and ITU Vehicular B channel model. Some simulation results are provided to illustrate the effectiveness of the proposed equalizer.

Personalised attraction recommendation for enhancing topic diversity and accuracy

2021 ◽  
pp. 016555152199980
Author(s):  
Yuanyuan Lin ◽  
Chao Huang ◽  
Wei Yao ◽  
Yifei Shao
Keyword(s):  
Real World ◽  
Information Overload ◽  
Two Stage ◽  
Misclassification Cost ◽  
Second Stage ◽  
Low Visibility ◽  
Recommendation Diversity ◽  
Optimisation Model ◽  
Definition Of ◽  
Improved Methods

Attraction recommendation plays an important role in tourism, such as solving information overload problems and recommending proper attractions to users. Currently, most recommendation methods are dedicated to improving the accuracy of recommendations. However, recommendation methods only focusing on accuracy tend to recommend popular items that are often purchased by users, which results in a lack of diversity and low visibility of non-popular items. Hence, many studies have suggested the importance of recommendation diversity and proposed improved methods, but there is room for improvement. First, the definition of diversity for different items requires consideration for domain characteristics. Second, the existing algorithms for improving diversity sacrifice the accuracy of recommendations. Therefore, the article utilises the topic ‘features of attractions’ to define the calculation method of recommendation diversity. We developed a two-stage optimisation model to enhance recommendation diversity while maintaining the accuracy of recommendations. In the first stage, an optimisation model considering topic diversity is proposed to increase recommendation diversity and generate candidate attractions. In the second stage, we propose a minimisation misclassification cost optimisation model to balance recommendation diversity and accuracy. To assess the performance of the proposed method, experiments are conducted with real-world travel data. The results indicate that the proposed two-stage optimisation model can significantly improve the diversity and accuracy of recommendations.

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