As one of the important ways to analyze discourse, thematic progression is an important way to organize news. It combines the thematic structure and textual coherence, revealing the dynamic distribution of theme and rheme in the discourse and the organization form of the discourse. As the carrier of information, news plays an essential role in people's daily life. The importance of news has drawn the attention of academic world. Thus, this paper aims to analyze the theme structure and thematic progression patterns in news reports to find out the preferred theme structures and thematic progression patterns. According to the analysis, it can be found that the simple theme is used more than the multiple theme, that the unmarked theme takes up the majority of all themes, and that the most frequently used pattern is the parallel pattern followed by the linear pattern. This study hopes to help editors and readers understand the inner structure of news deeply for effective news reading and news writing.
Abstract. Faced with massive news information, news readers hardly have enough time or energy to read all of them. It is news headlines that help them get the most useful and interesting information out of the flooded information. As the essential part of news, headlines aim to summarize the main content in concise language, attract readers' eyeballs and stimulate the enthusiasm of reading, which requires headlines of conciseness, vividness and attractiveness.While, pragmatic presupposition can just make it satisfied, and it's an effective device for news headlines writing. This thesis attempts to analyze the application of pragmatic presupposition in English news headlines, providing a new perspective to appreciate news headlines and helping readers comprehend news headlines in a profound way.
Data-driven models have been successfully applied to flood prediction. However, the nonlinearity and uncertainty of the prediction process and the possible noise or outliers in the data set will lead to incorrect results. In addition, data-driven models are only trained from available datasets and do not involve scientific principles or laws during the model training process, which may lead to predictions that do not conform to physical laws. To this end, we propose a flood prediction method based on data-driven and knowledge-guided heterogeneous graphs and temporal convolutional networks (DK-HTAN). In the data preprocessing stage, a low-rank approximate decomposition algorithm based on a time tensor was designed to interpolate the input data. Adding an attention mechanism to the heterogeneous graph module is beneficial for introducing prior knowledge. A self-attention mechanism with temporal convolutional network was introduced to dynamically calculate spatiotemporal correlation characteristics of flood data. Finally, we propose physical mechanism constraints for flood processes, adjusted and optimized data-driven models, corrected predictions that did not conform to physical mechanisms, and quantified the uncertainty of predictions. The experimental results on the Qijiang River Basin dataset show that the model has good predictive performance in terms of interval prediction index (PI), RMSE, and MAPE.
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