This paper studies the problem of extracting Chinese comparative sentences from user reviews, which is a problem of text classification in the level of sentence. This paper first deals with the class skewed problem of review data, and then builds a SVM (support vector machine) model to classify comparative and non-comparative sentences into different groups on a balanced dataset. Various linguistic and statistical features are introduced to characterize a sentence. Experiments were conducted on user-generated product reviews. As a result, our experiments show significant performance, an overall Fscore of 85.87%.
Abstract-With the decline of Chinese character thought andChinese language learning craze, we hope to help Chinese learners cultivate Chinese character thought imperceptibly and improve the cognition efficiency of Chinese characters on entertainment. On the basis of principles of Chinese characters and the experimental Cognitive results from Psychologist, we put forward a design method about Chinese character modules. Modules combinations decide on the location of modules. We present the partition design to retrieve the location of modules. Than measuring the results of the module combination by preset value will provide immediate feedback to users. In order to illustrate the feasibility of the method, we develop the Chinese characters game in smart mobile terminal which in the form of riddle to enhance entertainment. We expect that the method will explore a new type of digital mobile learning in Chinese subject construction. Intensive study about partition design will make more Chinese characters applicable on this system.
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