[Objective] To examine the grammar model based on lexical substring exac- tion for RNA secondary structure prediction. [Method] By introducing cloud model into stochastic grammar model, a machine learning algorithm su...[Objective] To examine the grammar model based on lexical substring exac- tion for RNA secondary structure prediction. [Method] By introducing cloud model into stochastic grammar model, a machine learning algorithm suitable for the lexicalized stochastic grammar model was proposed. The word grid mode was used to extract and divide RNA sequence to acquire lexical substring, and the cloud classifier was used to search the maximum probability of each lemma which was marked as a certain sec- ondary structure type. Then, the lemma information was introduced into the training stochastic grammar process as prior information, realizing the prediction on the sec- ondary structure of RNA, and the method was tested by experiment. [Result] The experimental results showed that the prediction accuracy and searching speed of stochastic grammar cloud model were significantly improved from the prediction with simple stochastic grammar. [Conclusion] This study laid the foundation for the wide application of stochastic grammar model for RNA secondary structure prediction.展开更多
This study explores the effects of syllable structures on the perception of L2 English lexical stress among Chinese junior ELF learners.Specifically,it focuses on to what extent syllable numbers and syllable patterns ...This study explores the effects of syllable structures on the perception of L2 English lexical stress among Chinese junior ELF learners.Specifically,it focuses on to what extent syllable numbers and syllable patterns differentiate the subjects’perception of target English lexical stress.The subjects were 93 junior middle-school students from two natural classes in Grade 7.The target lexical stress was the primarily stress placed on the 1;and 2;syllables respectively,which were embedded in two kinds of carrier words(nonsense&real English words)with three types of syllabic patterns(CVC.V,CVC.VC&CVC.CVC).The subjects first listened to the stimuli read by a British RP phonetician in falling tone and then did the perception tests in the pen-and-paper manner.Their correct perception ratios(CPRs)of the target English lexical stress were calculated and compared according to syllable numbers and syllable patterns.The results reveal that the CPRs of L2 English lexical stress in disyllabic words significantly outperformed those in trisyllabic words,and that among the disyllabic words,the CPRs ranked top,second,and bottom with the disyllabic patterns of CVC.V,CVC.CVC,and CVC.VC,respectively.The findings provide considerable evidence for the impact of syllabic structures on teenage Mandarin listeners’perception of L2 English lexical stress.展开更多
基金Supported by the Science Foundation of Hengyang Normal University of China(09A36)~~
文摘[Objective] To examine the grammar model based on lexical substring exac- tion for RNA secondary structure prediction. [Method] By introducing cloud model into stochastic grammar model, a machine learning algorithm suitable for the lexicalized stochastic grammar model was proposed. The word grid mode was used to extract and divide RNA sequence to acquire lexical substring, and the cloud classifier was used to search the maximum probability of each lemma which was marked as a certain sec- ondary structure type. Then, the lemma information was introduced into the training stochastic grammar process as prior information, realizing the prediction on the sec- ondary structure of RNA, and the method was tested by experiment. [Result] The experimental results showed that the prediction accuracy and searching speed of stochastic grammar cloud model were significantly improved from the prediction with simple stochastic grammar. [Conclusion] This study laid the foundation for the wide application of stochastic grammar model for RNA secondary structure prediction.
基金supported by Project G2021901022 from China’s Ministry of ScienceTechnology and Social Science Foundation of Jiangsu Province 18YYB009/20YYC018
文摘This study explores the effects of syllable structures on the perception of L2 English lexical stress among Chinese junior ELF learners.Specifically,it focuses on to what extent syllable numbers and syllable patterns differentiate the subjects’perception of target English lexical stress.The subjects were 93 junior middle-school students from two natural classes in Grade 7.The target lexical stress was the primarily stress placed on the 1;and 2;syllables respectively,which were embedded in two kinds of carrier words(nonsense&real English words)with three types of syllabic patterns(CVC.V,CVC.VC&CVC.CVC).The subjects first listened to the stimuli read by a British RP phonetician in falling tone and then did the perception tests in the pen-and-paper manner.Their correct perception ratios(CPRs)of the target English lexical stress were calculated and compared according to syllable numbers and syllable patterns.The results reveal that the CPRs of L2 English lexical stress in disyllabic words significantly outperformed those in trisyllabic words,and that among the disyllabic words,the CPRs ranked top,second,and bottom with the disyllabic patterns of CVC.V,CVC.CVC,and CVC.VC,respectively.The findings provide considerable evidence for the impact of syllabic structures on teenage Mandarin listeners’perception of L2 English lexical stress.
