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Titlebook: Experimental IR Meets Multilinguality, Multimodality, and Interaction; 14th International C Avi Arampatzis,Evangelos Kanoulas,Nicola Ferro

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樓主: Bunion
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發(fā)表于 2025-3-23 13:12:15 | 只看該作者
12#
發(fā)表于 2025-3-23 15:39:53 | 只看該作者
Cross-Lingual Candidate Retrieval and?Re-ranking for?Biomedical Entity Linkingdical datasets with grounded entity mentions for non-English languages are available for training supervised machine learning models. Moreover, the majority of concept aliases in medical vocabularies are also only available in English..In this work, we consider the problem of linking disease mention
13#
發(fā)表于 2025-3-23 20:18:49 | 只看該作者
Humour Translation with?Transformerst Transformer-based models are used to solve the three tasks introduced in the JOKER CLEF workshop. The Transformer model is a kind of neural network that tries to learn the contextual information from the sequential data by implicitly comprehending the existing relationships. In task 1, given a pie
14#
發(fā)表于 2025-3-23 22:34:43 | 只看該作者
15#
發(fā)表于 2025-3-24 02:36:05 | 只看該作者
A Re-labeling Approach Based on?Approximate Nearest Neighbors for?Identifying Gambling Disorders in? training dataset of a classification task, from a user-based annotation to a message-based one. In particular, we tackle Task 1 of the CLEF 2022 eRisk Workshop which consists in the processing of messages written by Social Media users, in order to detect early signs of pathological gambling. Our pr
16#
發(fā)表于 2025-3-24 06:33:49 | 只看該作者
Touché 2022 Best of?Labs: Neural Image Retrieval for?Argumentation support a discussion on the topic. This paper provides a detailed investigation of the challenges of this task by means of a novel and modular retrieval pipeline. All findings relate to our work from last year’s CLEF Touché’22 lab and a reproducibility study based on it. There, we demonstrate the u
17#
發(fā)表于 2025-3-24 11:22:18 | 只看該作者
18#
發(fā)表于 2025-3-24 17:31:36 | 只看該作者
19#
發(fā)表于 2025-3-24 20:31:55 | 只看該作者
20#
發(fā)表于 2025-3-25 03:05:06 | 只看該作者
Genomics Approaches To Soybean Improvement, the description of the code, 2) to reorganize the code using a better reproducibility approach (R markdown), 3) to propose some minor changes to the code that would improve the performances of the system.
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