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  • Open Access English
    Authors: 
    Theodoros Giannakopoulos; Ioannis Foufoulas; Elefterios Stamatogiannakis; Harry Dimitropoulos; Natalia Manola; Yannis Ioannidis;
    Publisher: Corporation for National Research Initiatives
    Project: WT , EC | OPENAIREPLUS (283595)

    Text visualization is a rather important task related to scientific corpora, since it provides a way of representing these corpora in terms of content, leading to reinforcement of human cognition compared to abstract and unstructured text. In this paper, we focus on visualizing funding-specific scientific corpora in a supervised context and discovering interclass similarities which indicate the existence of inter-disciplinary research. This is achieved through training a supervised classification � visualization model based on the arXiv classification system. In addition, a funding mining submodule is used which identifies documents of particular funding schemes. This is conducted, in order to generate corpora of scientific publications that share a common funding scheme (e.g. FP7-ICT). These categorized sets of documents are fed as input to the visualization model in order to generate content representations and to discover highly correlated content classes. This procedure can provide a high level monitoring which is important for research funders and governments in order to be able to quickly respond to new developments and trends.

Include:
1 Research products, page 1 of 1
  • Open Access English
    Authors: 
    Theodoros Giannakopoulos; Ioannis Foufoulas; Elefterios Stamatogiannakis; Harry Dimitropoulos; Natalia Manola; Yannis Ioannidis;
    Publisher: Corporation for National Research Initiatives
    Project: WT , EC | OPENAIREPLUS (283595)

    Text visualization is a rather important task related to scientific corpora, since it provides a way of representing these corpora in terms of content, leading to reinforcement of human cognition compared to abstract and unstructured text. In this paper, we focus on visualizing funding-specific scientific corpora in a supervised context and discovering interclass similarities which indicate the existence of inter-disciplinary research. This is achieved through training a supervised classification � visualization model based on the arXiv classification system. In addition, a funding mining submodule is used which identifies documents of particular funding schemes. This is conducted, in order to generate corpora of scientific publications that share a common funding scheme (e.g. FP7-ICT). These categorized sets of documents are fed as input to the visualization model in order to generate content representations and to discover highly correlated content classes. This procedure can provide a high level monitoring which is important for research funders and governments in order to be able to quickly respond to new developments and trends.

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