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Information Retrieval in large digital document repositories is at the same time a hard and crucial task. While the primary type of information available in documents is usually text, images play a very important role because they pictorially describe concepts that are dealt with in the document. Unfortunately, the semantic gap separating such a visual content from the underlying meaning is very wide...
Effective information retrieval in digital libraries requires semantic alignments of documents with taxonomy. The alignments provide the semantic description of documents. The proposed methodology aligns documents using the hierarchical structure of taxonomy. It refines the results of the existing semantic key phrase extraction algorithm. The evaluation shows promising results.
This article presents a framework for automatic semantic annotation of video streams with an ontology that includes concepts expressed using linguistic terms and visual data.
Topic digital library is a special domain digital library based on topic features. This paper is to introduce a new approach to build topic navigation in the topic digital library using topic extraction and clustering. Topic digital library is an important application of knowledge service and it is a special domain digital library based on topic or concept features. Firstly, documents in a special...
This paper is to introduce a new approach to build topic digital library using concept extraction and document clustering. Firstly, documents in a special domain are automatically produced by document classification approach. Then, the keywords of each document are extracted using the machine learning approach. The keywords are used to cluster the documents subset. The clustered result is the taxonomy...
The domain of Digital Libraries presents specific challenges for unsupervised information extraction to support both the automatic classification of documents and the enhancement of userspsila navigation in the digital content. In this paper, we propose a combined use of machine learning techniques (i.e. Support Vector Machines) and Natural Language Processing techniques (i.e. Stanford NLP parser)...
Bibliographic databases are indispensable to digital libraries for academic articles. However, extracting bibliographic elements from printed documents requires a lot of human intervention; it is not cost-effective, even when using various document image-processing techniques such as optical character recognition (OCR). In this paper, we propose an automatic bibliographic element extraction method...
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