Titlebook: Analysis of Images, Social Networks and Texts; 9th International Co Wil M. P. van der Aalst,Vladimir Batagelj,Elena Tu Conference proceedin

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https://doi.org/10.1007/978-3-8348-9038-2stly, developers of book recommendation systems and electronic libraries may be interested in filtering texts by the age of the most likely readers. Further, parents may want to select literature for children. Finally, it will be useful for writers and publishers to determine which features influenc

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Programmierbare Logikbausteine,from the most popular Russian messaging/social networking services (Telegram, VK) was compiled semi-automatically. Emojis contained in the text messages were used to annotate the data for emotions expressed. This paper proposes an integrated approach to text-based emotion classification combining li

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Programmierbare Logikbausteine, Russian. We run an extensive series of experiments of modern extractive and abstractive approaches. The results demonstrate that BERT-based models show modest performance, reaching up?to 0.26 ROUGE-1F-measure. In addition, human evaluation shows that neural approaches could generate feasible althou

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https://doi.org/10.1007/978-3-8348-9370-3hem, particularly between the arguments of a predicate. For this purpose, the RuSentiFrames lexicon was created. But the training of the ML model requires an annotated collection of data, and since the manual annotation is laborious and expensive, the automation of the process is preferable. In this

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Programmierbare Logikbausteine,how that the Sequence Generating BERT model achieves decent results in significantly fewer training epochs compared to the standard BERT. We also introduce and experimentally examine a mixed model, an ensemble of BERT and Sequence Generating BERT models. Our experiments demonstrate that the proposed

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Analog-Digital- und Digital-Analog-Umsetzer,e suspicious lesions detection stage. Contrary to typical decisions in medical image analysis, the proposed approach considers input data not as a 2D or 3D image, but rather as a point cloud, and uses deep learning models for point clouds. We discovered that point cloud models require less memory an

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Lecture Notes in Computer Sciencehttp://image.papertrans.cn/a/image/156376.jpg