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Titlebook: Experimental IR Meets Multilinguality, Multimodality, and Interaction; 11th International C Avi Arampatzis,Evangelos Kanoulas,Nicola Ferro

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發(fā)表于 2025-3-23 11:21:05 | 只看該作者
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發(fā)表于 2025-3-23 15:09:30 | 只看該作者
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發(fā)表于 2025-3-23 19:53:26 | 只看該作者
Twitter User Profiling: Bot and Gender Identification audience to influence opinions, often politically motivated, or to promote individuals to appear more popular than they really are. We proposed a set of feature extraction and transformation methods in conjunction with ensemble classifiers for the PAN 2019 Author Profiling task. For the bot identif
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發(fā)表于 2025-3-24 01:05:23 | 只看該作者
Medical Image Tagging by Deep Learning and Retrieval report abnormalities in medical images could speed up diagnostic workflows. Systems that would help exploit past diagnoses made by highly skilled physicians could also benefit their more junior colleagues. A task that systems can perform towards this end is medical image classification, which assig
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發(fā)表于 2025-3-24 04:18:53 | 只看該作者
Overview of ARQMath 2020: CLEF Lab on Answer Retrieval for Questions on Mathk Exchange). Queries are question posts held out from the searched collection, each containing both text and at least one formula. This is a challenging task, as both math and text may be needed to find relevant answer posts. ARQMath also includes a formula retrieval sub-task: individual formulas fr
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發(fā)表于 2025-3-24 08:30:15 | 只看該作者
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發(fā)表于 2025-3-24 14:07:40 | 只看該作者
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發(fā)表于 2025-3-24 15:35:34 | 只看該作者
Francesco Dramis,Giandomenico Fubellir a fixed amount or a variable amount of resources according to the size of the pool. The results show that our approach performs on average much better than any other participant in the CLEF 2019 eHealth TAR task. Nevertheless, a failure analysis allows to understand the weak points of this approach and possible future directions.
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發(fā)表于 2025-3-24 22:19:41 | 只看該作者
20#
發(fā)表于 2025-3-25 02:46:08 | 只看該作者
Question Answering When Knowledge Bases are Incompletethe code to reproduce experiments are available at ..). Our dataset, called IDK (Incomplete Data in Knowledge base question answering), allows to perform studies on how to detect and recover from such cases. The analysis shows that simple baselines fail to detect most of the unanswerable questions.
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