![]() ![]() Examples of such research networks are Mendeley and CiteULike. Researchers are able to manage their collection of scientific articles and exchange and discuss papers with colleagues. In recent years social research networks have gained a lot of momentum. Keywords: Meta-data Extraction, Supervised Machine Learning, Content Analysis and Indexing, Natural Language Processing, Text Analysis ![]() TeamBeam performs well under testing and compares favourably with existing approaches. Three different data sets with varying characteristics are used to assess the quality of the extraction results. In the evaluation of the algorithm, its performance is compared against two heuristics and three existing meta-data extraction systems. A classification algorithm, which takes the sequence of the input into account, is then applied in two consecutive phases. The input of the algorithm is a set of blocks generated from the article text. 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. ![]() In such settings, meta-data is rarely explicitly provided, leading to the need for automatically extracting this valuable information. Meta-data plays an important role in providing services to retrieve and organise the articles. Social research networks, such as Mendeley and CiteULike, provide services that support this task. An important aspect of the work of researchers as well as librarians is to manage collections of scientific literature. ![]()
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