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51#
發(fā)表于 2025-3-30 10:21:58 | 只看該作者
I, Me, Mine: The Role of Personal Phrases in Author Profilingned by personal phrases considerably outperformed those from non-personal sentences, indicating their greater suitability for the AP task. We consider these findings could be further applied in the design of strategies for the construction of AP corpora, novel feature selection methods, as well as n
52#
發(fā)表于 2025-3-30 15:36:03 | 只看該作者
Improving Profiles of Weakly-Engaged Usersofile. Experiments on two datasets, news and video, from a popular online portal show improvements of up?to more than 100?% in terms of MAP for extremely weak profiles, and up?to around 10?% for moderately weak profiles. In order to evaluate the impact of our method on learned latent space embedding
53#
發(fā)表于 2025-3-30 17:26:49 | 只看該作者
A Two-Step Retrieval Method for Image Captioninged the visual representation of images to learn a feature model, in this way we can match query images with words by simply measuring visual similarity. Second, a query is formed with the retrieved words and candidate captions are retrieved from a reference dataset of sentences. Despite the simplici
54#
發(fā)表于 2025-3-30 21:03:55 | 只看該作者
Concept Recognition in French Biomedical Text Using Automatic Translationith concepts from the UMLS, and split in an equally-sized training and test set. The best performance on the training set was obtained with a terminology that contained the intersection of the translated terms in combination with several post-processing steps to reduce the number of false-positive d
55#
發(fā)表于 2025-3-31 04:41:04 | 只看該作者
56#
發(fā)表于 2025-3-31 05:08:55 | 只看該作者
Francisco Gutiérrez,Mateo Gutiérreze parliament. Our main findings are the following. First, we define hierarchical significant words language models as an iterative estimation process across the hierarchy, resulting in tiny models capturing only well grounded text features at each level. Second, we apply the resulting models to part
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