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樓主: Disaster
41#
發(fā)表于 2025-3-28 15:32:16 | 只看該作者
I, Me, Mine: The Role of Personal Phrases in Author Profilingtudying the language usage. In this work we studied the role of personal phrases (i.e., sentences containing first person pronouns) for the AP task. We support the idea that people better expose their personal interests and writing style when they talk about themselves and, consequently, that words
42#
發(fā)表于 2025-3-28 19:15:51 | 只看該作者
43#
發(fā)表于 2025-3-29 02:40:46 | 只看該作者
Kronecker Decomposition for Image Classification between the elements of the image and their distance. Images decomposed this way are then classified using a maximum margin regression (MMR) approach where the normal vector of the separating hyperplane maps the input feature vectors into the outputs vectors. Multiclass and multilabel classificatio
44#
發(fā)表于 2025-3-29 04:59:42 | 只看該作者
45#
發(fā)表于 2025-3-29 08:35:39 | 只看該作者
Concept Recognition in French Biomedical Text Using Automatic Translationis community challenge included recognition of entities in a French medical corpus, normalization of the recognized entities, and normalization of entity mentions that had been manually annotated. Normalization had to be based on the Unified Medical Language System (UMLS). We addressed all three sub
46#
發(fā)表于 2025-3-29 12:47:13 | 只看該作者
47#
發(fā)表于 2025-3-29 16:56:07 | 只看該作者
48#
發(fā)表于 2025-3-29 21:45:04 | 只看該作者
49#
發(fā)表于 2025-3-30 01:40:54 | 只看該作者
Two-Way Parsimonious Classification Models for?Evolving Hierarchiese 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
50#
發(fā)表于 2025-3-30 07:38:59 | 只看該作者
Predicting Contextually Appropriate Venues in?Location-Based Social Networksy the set of features appropriate for our problem and to evaluate the effectiveness of our proposed approach. Our results demonstrate both the accuracy of our classification approach in predicting suitable contextual aspects for a venue, and its effectiveness at making better venue recommendations t
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