The paper applies topic modeling to the collection of ERC-funded proposals, interim reports and relative publications, with the aim of measuring in a novel way the degree of interdisciplinarity and addressing several open research questions which broadly aim at understanding how environmental conditions can favour the blossoming of interdisciplinarity. Without venturing into potential interpretations and explanations, we present a series of quantitative results linked with the above questions, while deliberately maintaining a descriptive attitude.

Detecting interdisciplinarity in top-class research using topic modeling / Bonaccorsi, A.; Melluso, Nicola; Massucci, F. A.. - 18th International Conference on Scientometrics and Informetrics, ISSI 2021, (2021), pp. 169-180. (18th International Conference on Scientometrics and Informetrics Conference, ISSI 2021, KU Leuven, Belgium, July 12-15, 2021).

Detecting interdisciplinarity in top-class research using topic modeling

Melluso N.;
2021

Abstract

The paper applies topic modeling to the collection of ERC-funded proposals, interim reports and relative publications, with the aim of measuring in a novel way the degree of interdisciplinarity and addressing several open research questions which broadly aim at understanding how environmental conditions can favour the blossoming of interdisciplinarity. Without venturing into potential interpretations and explanations, we present a series of quantitative results linked with the above questions, while deliberately maintaining a descriptive attitude.
2021
9789080328228
Text Mining, Natural Language Processing, Interdisciplinarity, Topic Modeling
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11385/248990
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