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Titlebook: Research and Advanced Technology for Digital Libraries; 21st International C Jaap Kamps,Giannis Tsakonas,Ioannis Karydis Conference proceed

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樓主: otitis-externa
41#
發(fā)表于 2025-3-28 18:07:07 | 只看該作者
RussianFlu-DE: A German Corpus for a Historical Epidemic with Temporal Annotationctions to them, and to construct timelines of events and of language use, among other applications. However, producing a precise corpus of a particular event in history is very challenging due to the lack of noise-free digitalized data. This paper introduces RussianFlu-DE, a temporally annotated cor
42#
發(fā)表于 2025-3-28 22:05:00 | 只看該作者
43#
發(fā)表于 2025-3-28 23:05:18 | 只看該作者
44#
發(fā)表于 2025-3-29 04:01:03 | 只看該作者
Automatic Hierarchical Categorization of Research Expertise Using Minimum Informationcher with such areas makes a series of tasks feasible such as the organization of digital repositories, expertise recommendation and the formation of research groups for complex problems. In this paper we propose a simple yet effective automatic classification model that is capable of categorizing r
45#
發(fā)表于 2025-3-29 08:53:59 | 只看該作者
Extracting Event-Centric Document Collections from Large-Scale Web Archivesentists. These collections constitute an increasingly important source for researchers in the social sciences, the historical sciences and journalists interested in studying past events. However, there are currently no access methods that help users to efficiently access information, in particular a
46#
發(fā)表于 2025-3-29 11:23:23 | 只看該作者
47#
發(fā)表于 2025-3-29 15:43:48 | 只看該作者
48#
發(fā)表于 2025-3-29 21:03:59 | 只看該作者
How Linked Data can Aid Machine Learning-Based Tasksg and preparing data before using them for various reasons, e.g., for applying or testing machine learning algorithms. In this paper we propose a general method for discovering, creating and selecting, in an easy way, valuable features describing a set of entities for leveraging them in a machine le
49#
發(fā)表于 2025-3-30 01:59:09 | 只看該作者
Can Plausibility Help to Support High Quality Content in Digital Libraries? contrasted with prior knowledge. In particular, our work proposes a novel assessment of scientific papers to support the workload of reviewers. The proposed approach focus on a core component of a scientific paper: its claim. Our methodology exploits state of the art neural embedding representation
50#
發(fā)表于 2025-3-30 04:28:06 | 只看該作者
Classifying Document Types to Enhance Search and Recommendations in Digital Libraries. We show that the metadata provided by repositories enabling us to distinguish research papers, thesis and slides are missing in over . of cases. While these metadata describing document types are useful in a variety of scenarios ranging from research analytics to improving search and recommender (
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