We introduce a novel Sermntic-Category- Tree （SCT） model to present the sen-antic structure of a sentence for Chinese-English Machine Translation （MT）. We use the SCT model to handle the reordering in a hierarchic...We introduce a novel Sermntic-Category- Tree （SCT） model to present the sen-antic structure of a sentence for Chinese-English Machine Translation （MT）. We use the SCT model to handle the reordering in a hierarchical structure in which one reordering is dependent on the others. Different from other reordering approaches, we handle the reordering at three levels： sentence level, chunk level, and word level. The chunk-level reordering is dependent on the sentence-level reordering, and the word-level reordering is dependent on the chunk-level reordering. In this paper, we formally describe the SCT model and discuss the translation strategy based on the SCT model. Further, we present an algorithm for analyzing the source language in SCT and transforming the source SCT into the target SCT. We apply the SCT model to a role-based patent text MT to evaluate the ability of the SCT model. The experimental results show that SCT is efficient in handling the hierarehical reordering operation in MT.展开更多
Objective:To establish a systematic framework for selecting the best clustering algorithm and provide an evaluation method for clustering analyses of gene expression data. Methods: Based on data structure (internal in...Objective:To establish a systematic framework for selecting the best clustering algorithm and provide an evaluation method for clustering analyses of gene expression data. Methods: Based on data structure (internal information) and function classification (external information), the evaluation of gene expression data analyses were carried out by using 2 approaches. Firstly, to assess the predictive power of clusteringalgorithms, Entropy was introduced to measure the consistency between the clustering results from different algorithms and the known and validated functional classifications. Secondly, a modified method of figure of merit (adjust-FOM) was used as internal assessment method. In this method, one clustering algorithm was used to analyze all data but one experimental condition, the remaining condition was used to assess the predictive power of the resulting clusters. This method was applied on 3 gene expression data sets (2 from the Lyer's Serum Data Sets, and 1 from the Ferea's Saccharomyces Cerevisiae Data Set). Results: A method based on entropy and figure of merit (FOM) was proposed to explore the results of the 3 data sets obtained by 6 different algorithms, SOM and Fuzzy clustering methods were confirmed to possess the highest ability to cluster. Conclusion: A method based on entropy is firstly brought forward to evaluate clustering analyses.Different results are attained in evaluating same data set due to different function classification. According to the curves of adjust_FOM and Entropy_FOM, SOM and Fuzzy clustering methods show the highest ability to cluster on the 3 data sets.展开更多
基金supported by the National High Technology Research and Development Program of China under Grant No.2012AA011104the Fundamental Research Funds for the Center Universities
文摘We introduce a novel Sermntic-Category- Tree （SCT） model to present the sen-antic structure of a sentence for Chinese-English Machine Translation （MT）. We use the SCT model to handle the reordering in a hierarchical structure in which one reordering is dependent on the others. Different from other reordering approaches, we handle the reordering at three levels： sentence level, chunk level, and word level. The chunk-level reordering is dependent on the sentence-level reordering, and the word-level reordering is dependent on the chunk-level reordering. In this paper, we formally describe the SCT model and discuss the translation strategy based on the SCT model. Further, we present an algorithm for analyzing the source language in SCT and transforming the source SCT into the target SCT. We apply the SCT model to a role-based patent text MT to evaluate the ability of the SCT model. The experimental results show that SCT is efficient in handling the hierarehical reordering operation in MT.
文摘Objective:To establish a systematic framework for selecting the best clustering algorithm and provide an evaluation method for clustering analyses of gene expression data. Methods: Based on data structure (internal information) and function classification (external information), the evaluation of gene expression data analyses were carried out by using 2 approaches. Firstly, to assess the predictive power of clusteringalgorithms, Entropy was introduced to measure the consistency between the clustering results from different algorithms and the known and validated functional classifications. Secondly, a modified method of figure of merit (adjust-FOM) was used as internal assessment method. In this method, one clustering algorithm was used to analyze all data but one experimental condition, the remaining condition was used to assess the predictive power of the resulting clusters. This method was applied on 3 gene expression data sets (2 from the Lyer's Serum Data Sets, and 1 from the Ferea's Saccharomyces Cerevisiae Data Set). Results: A method based on entropy and figure of merit (FOM) was proposed to explore the results of the 3 data sets obtained by 6 different algorithms, SOM and Fuzzy clustering methods were confirmed to possess the highest ability to cluster. Conclusion: A method based on entropy is firstly brought forward to evaluate clustering analyses.Different results are attained in evaluating same data set due to different function classification. According to the curves of adjust_FOM and Entropy_FOM, SOM and Fuzzy clustering methods show the highest ability to cluster on the 3 data sets.
