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Titlebook: Experimental IR Meets Multilinguality, Multimodality, and Interaction; 9th International Co Patrice Bellot,Chiraz Trabelsi,Nicola Ferro Con

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31#
發(fā)表于 2025-3-26 21:02:29 | 只看該作者
32#
發(fā)表于 2025-3-27 03:19:21 | 只看該作者
33#
發(fā)表于 2025-3-27 06:54:26 | 只看該作者
Deep Multimodal Classification of Image Types in Biomedical Journal Figuression methods are analyzed as well as data augmentation approaches. The proposed system is validated on the ImageCLEF 2013 and 2016 figure and subfigure classification tasks, largely improving the currently best performance from 83.5% to 93.7% accuracy and 88.4% to 89.0% respectively.
34#
發(fā)表于 2025-3-27 11:47:23 | 只看該作者
35#
發(fā)表于 2025-3-27 14:16:10 | 只看該作者
Using R Markdown for Replicable Experiments in Evidence Based Medicinelevant medical documents, given an information need, with the least effort. We study how lay people, students of a master degree in languages in this case, can help the retrieval system in finding more relevant documents by means of a query rewriting approach.
36#
發(fā)表于 2025-3-27 18:59:18 | 只看該作者
Automatic Query Selection for?Acquisition and Discovery of?Food-Drug Interactionsy that aims to automatically identify scientific publications that describe food-drug interactions from a database of biomedical literature. We make use of an expert curated corpus of food-drug interactions to analyse different methods for query selection and we propose a high-recall approach based on feature selection.
37#
發(fā)表于 2025-3-28 01:06:41 | 只看該作者
Ganapathy Kuyyamudi Nanaiah,Sujay Rakshitlicity, it is fast and can be scaled to large datasets. Experimental results on several real-world datasets demonstrate that the proposed method has overall better performance compared to several deterministic, random, or order-sensitive methods in terms of clustering quality and runtime.
38#
發(fā)表于 2025-3-28 02:46:02 | 只看該作者
39#
發(fā)表于 2025-3-28 08:52:27 | 只看該作者
40#
發(fā)表于 2025-3-28 11:01:32 | 只看該作者
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