Using an integrated feature set to generalize and justify the Chinese-to-English transferring rule of the 'ZHE' aspect

In machine translation(MT) practice,there is an urgent need for constructing a set of Chinese-to-English aspect transferring rules to define the transferring conditions.The integrated feature set was used to generalize and justify the Chinese-to-English transferring rule of the 'ZHE' aspect(ZHE Rule).A ZHE classification model was built in this study.The impacts of each set of temporal,lexical aspectual,and syntactic features,and their integrated impacts,on the accuracy of the ZHE Rule were tested.Over 600 misclassified corpus sentences were manually examined.A 10-fold cross-validation was used with a decision tree algorithm.The main results are:(1) The ZHE Rule was generalized and justified to have a higher accuracy under the two metrics:the precision rate and the areas under the receiver operating characteristic curve(AUC).(2) The temporal,lexical aspectual,and syntactic feature sets have an integrated contribution to the accuracy of the ZHE Rule.The syntactic and temporal features have an impact on ZHE aspect derivations,while the lexical aspectual features are not predictive of ZHE aspect derivation.(3) While associated with active verbs,the ZHE aspect can denote a perfective situation.This study suggests that the temporal and syntactic features are the predictive ZHE aspect classification features and that the ZHE Rule with an overall precision rate of 80.1% is accurate enough to be further explored in MT practice.The machine learning method,decision tree,can be applied to the automatic aspect transferring in MT research and aspectual interpretations in linguistic research.

Consistency of weighted feature set and polyspectral kernels in individual communication transmitter identification

This paper presents a method using support vector machine with polyspectral kernels for classification of individual transmitters.Then,the neighborhood-roughset-based weighted feature set is proposed.The experiments of the algorithms mentioned above indicate that they have consistency,which raises a new weighted kernel.The experiment shows that better classification rate can be achieved.

Dual-modal Physiological Feature Fusion-based Sleep Recognition Using CFS and RF Algorithm

Research has demonstrated a significant overlap between sleep issues and other medical conditions.In this paper,we consider mild difficulty in falling asleep(MDFA).Recognition of MDFA has the potential to assist in the provision of appropriate treatment plans for both sleep issues and related medical conditions.An issue in the diagnosis of MDFA lies in subjectivity.To address this issue,a decision support tool based on dual-modal physiological feature fusion which is able to automatically identify MDFA is proposed in this study.Special attention is given to the problem of how to extract candidate features and fuse dual-modal features.Following the identification of the optimal feature set,this study considers the correlations between each feature and class and evaluates correlations between the inter-modality features.Finally,the recognition accuracy was measured using 10-fold cross validation.The experimental results for our method demonstrate improved performance.The highest recognition rate of MDFA using the optimal feature set can reach 96.22%.Based on the results of current study,the authors will,in projected future research,develop a real-time MDFA recognition system.