We present a framework for keyphrase extraction from scientific journals in diverse research fields. While journal articles are often provided with manually assigned keywords, it is not clear how to automatically extract keywords and measure their significance for a set of journal articles. We compare extracted keyphrases from journals in the fields of astrophysics, mathematics, physics, and computer science. We show that the presented statistics-based framework is able to demonstrate differences among journals, and that the extracted keyphrases can be used to represent journal or conference research topics, dynamics, and specificity.
CITATION STYLE
Daudaravicius, V. (2016). A framework for keyphrase extraction from scientific journals. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9792 LNCS, pp. 51–66). Springer Verlag. https://doi.org/10.1007/978-3-319-53637-8_7
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