In such settings, meta-data is rarely explicitly provided, leading to the need for automatically extracting this valuable information. The TeamBeam algorithm analyses a scientific article and extracts structured meta-data, such as the title, journal name and abstract, as well as information about the article's authors (e.g. The combination of namespace and name is the unique identifier for a userscript. JabRef ist ein MIT-lizenzierter Open Source-Bibliografiemanager für BibTeX und BibLaTeX entwickelt auf GitHub. Add it to your config.yml: plugins: - 'jekyll-github-metadata'. The input of the algorithm is a set of blocks generated from the article text. namespace can be any string, for example the homepage of a group of userscripts by the same author. If not provided the namespace falls back to an empty string ( '' ). Es bietet die Funktionalität zum Importieren bibliografischer Daten aus PDFs. Text mining and information retrieval in large collections of scientific literature require automated processing systems that analyse the documents’ content. :warning: If you are using Jekyll < 3.5.0, use the gems key instead of plugins. However, the layout of scientific articles is highly varying across publishers, and common digital document formats are optimised for presentation, but lack structural information. Now, whenever you build or serve with Jekyll, the jekyll-github-metadata plugin will run. A classification algorithm, which takes the sequence of the input into account, is then applied in two consecutive phases. ![]() In the evaluation of the algorithm, its performance is compared against two heuristics and three existing meta-data extraction systems. Three different data sets with varying characteristics are used to assess the quality of the extraction results. TeamBeam performs well under testing and compares favourably with existing approaches.
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