Search (2 results, page 1 of 1)

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Abstract

Currently, web document repositories have been collaboratively created and edited. One of these repositories, Wikipedia, is facing an important problem: assessing the quality of Wikipedia. Existing approaches exploit techniques such as statistical models or machine leaning algorithms to assess Wikipedia article quality. However, existing models do not provide satisfactory results. Furthermore, these models fail to adopt a comprehensive feature framework. In this article, we conduct an extensive survey of previous studies and summarize a comprehensive feature framework, including text statistics, writing style, readability, article structure, network, and editing history. Selected state-of-the-art deep-learning models, including the convolutional neural network (CNN), deep neural network (DNN), long short-term memory (LSTMs) network, CNN-LSTMs, bidirectional LSTMs, and stacked LSTMs, are applied to assess the quality of Wikipedia. A detailed comparison of deep-learning models is conducted with regard to different aspects: classification performance and training performance. We include an importance analysis of different features and feature sets to determine which features or feature sets are most effective in distinguishing Wikipedia article quality. This extensive experiment validates the effectiveness of the proposed model.

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Abstract

There is no information search system can assist young children's known picture book search needs since the information is not organized according to their cognitive abilities and needs. Therefore, this study explored young children's known picture book search strategies and extracted picture book search elements by simulating a search scenario and playing a picture book search game. The study found 29 elements children used to search for known picture books. Then, these elements are classified into three dimensions: The first dimension is the concept category of an element. The second dimension is an element's status in the story. The third dimension indicates where an element appears in a picture book. Additionally, it revealed a young children's general search strategy: Children first use auditory elements that they hear from the adults during reading. After receiving error returns, they add visual elements that they see by themselves in picture books. The findings can not only help to understand young children's known-item search and reformulation strategies during searching but also provide theoretical support for the development of a picture book information organization schema in the search system.