[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
Vocabulary knowledge is an important component of the writing skill and it has many dimensions, such as size, depth, and productive, in interaction with writing skill. To evaluate this relation and determine which dim...Vocabulary knowledge is an important component of the writing skill and it has many dimensions, such as size, depth, and productive, in interaction with writing skill. To evaluate this relation and determine which dimension is the most effective for second language writing quality, the present study was conducted. Turkish EFL （English as a Foreign Language） learners＇ lexical competence and writing abilities were examined through their vocabulary profiles and academic essays. The results of each vocabulary measure indicated that the participants had a limited vocabulary size, containing words mostly from 2,000 to 3,000 frequency bands and thus, the productive vocabulary knowledge of the participants mostly consisted of lk ＋ 2k words and the use of academic words in their essays was very low. The results of the study revealed that the lexical competence covering the main components of vocabulary knowledge was a good predictor of the students＇ quality of writing performance.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.
文摘Vocabulary knowledge is an important component of the writing skill and it has many dimensions, such as size, depth, and productive, in interaction with writing skill. To evaluate this relation and determine which dimension is the most effective for second language writing quality, the present study was conducted. Turkish EFL （English as a Foreign Language） learners＇ lexical competence and writing abilities were examined through their vocabulary profiles and academic essays. The results of each vocabulary measure indicated that the participants had a limited vocabulary size, containing words mostly from 2,000 to 3,000 frequency bands and thus, the productive vocabulary knowledge of the participants mostly consisted of lk ＋ 2k words and the use of academic words in their essays was very low. The results of the study revealed that the lexical competence covering the main components of vocabulary knowledge was a good predictor of the students＇ quality of writing performance.
