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Titlebook: Learning Structure and Schemas from Documents; Marenglen Biba,Fatos Xhafa Book 2011 Springer-Verlag GmbH Berlin Heidelberg 2011 Computatio

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樓主: Iridescent
51#
發(fā)表于 2025-3-30 09:21:27 | 只看該作者
Digital Libraries and Document Image Retrieval Techniques: A Survey,raditional libraries. Document images are intrinsically non-structured and the structure and semantic of the digitized documents is in most part lost during the conversion. Several techniques related to the Document Image Analysis research area have been proposed in the past to deal with document im
52#
發(fā)表于 2025-3-30 14:14:52 | 只看該作者
Mining Biomedical Text towards Building a Quantitative Food-Disease-Gene Network,To uncover the underlying knowledge base hidden in such data, text mining techniques have been utilized. Past and current efforts in this area have been largely focusing on recognizing gene and protein names, and identifying binary relationships among genes or proteins. In this chapter, we present a
53#
發(fā)表于 2025-3-30 17:58:24 | 只看該作者
54#
發(fā)表于 2025-3-30 23:16:36 | 只看該作者
55#
發(fā)表于 2025-3-31 04:37:17 | 只看該作者
Integrating Content and Structure into a Comprehensive Framework for XML Document Similarity Represiques capable of investigating the similarity between XML documents to help in classifying them for better organized utilization. In fact, the idea of similarity between documents is not new. However, XML documents are more rich and informative than classical documents in the sense that they encapsu
56#
發(fā)表于 2025-3-31 06:57:32 | 只看該作者
57#
發(fā)表于 2025-3-31 12:25:44 | 只看該作者
MANENT: An Infrastructure for Integrating, Structuring and Searching Digital Libraries,chival organisations, methods and resources thanks to systems relying on standard metadata formats. This chapter describes some natural language processing techniques exploited for automatically extracting structural information from documents stored in Digital Libraries, based on the exposed metada
58#
發(fā)表于 2025-3-31 14:07:04 | 只看該作者
59#
發(fā)表于 2025-3-31 19:44:57 | 只看該作者
Model Learning from Published Aggregated Data,andard deviations are widely available. This limitation is a result of many factors, including privacy laws that prevent clinicians and scientists from freely sharing individual patient data, inability to share proprietary business data, and inadequate data collection methods. Consequently, it preve
60#
發(fā)表于 2025-3-31 22:41:03 | 只看該作者
Data De-duplication: A Review,ge quantities of such information are stored as free texts. The lack of explicit structure in free text is a major issue in the categorization of such kind of data for more effective and efficient information retrieval, search and filtering. The abundance of structured data is problematic too. Sever
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