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Constructing Automated Leveling Models for Popular Science Articles
Popular Scientific Article
Latent Semantic Analysis
Support Vector Machine
Reading is beginning of learning, and also a primary tool for knowledge acquiring. Popular Science magazine is a kind of helpful reading materials for readers. It assists people to realize or to learn the basic conception and application of science and also cultivates public a scientific spirit to think, to probe into every phenomenon of daily life. The level of reading materials which matches readers’ ability and purpose bring readers the best benefits in reading. The domestic publishers of Popular Science magazine provide their products a reference of appropriate readers, however they usually draw a large range of suitable readers’ ages, for the reason that they can profit more from their customers. Another reason is when students have some problems on reading magazines, parents and teachers may give them some aids, so publishers extend the target objects and it caused there are not any exactly leveled books for readers. This study selected 150 Popular Scientific articles writing or translation in the Chinese language from three different versions of the magazine. With the use of readability text classification, which combines linguistic features and concept words that were displayed by a list and have degree of difficulty, to construct a two-stage leveling models for different reading needs of different grades students. Readability Assessment could quantify the difficulty of the text, then students could choose the appropriate reading articles. The corpus of this two stage automated leveling models is based on 12 grades textbooks, which contains Chinese, social studies, and natural science three subjects, it could predict difficulty levels of any articles whether from a book or internet in scientific disciplines, with the readers’ grade range from 1st grade at primary school to 13th grade at university. To compare with the result of leveling models, the researcher also invited nine natural science teachers from primary, junior high and senior high school to estimate the suitable readers’ grade of these Popular Scientific articles. The rate of models’ accuracy is 59.73% for the strict standard and 73.15% for the less stringent but acceptable standard. The models could supply public a more precisely and verified result.
|Appears in Collections:||學位論文|
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