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A preliminary study to compare deep learning with rule-based approaches for citation classification

Abstract : Categorization of semantic relationships between scientific papers is a key to characterize the condition of a research field and to identify influential works. Recently, new approaches based on Deep Learning have demonstrated good capacities to tackle Natural Language Processing problems, such as text classification and information extraction. In this paper, we show how deep learning algorithms can automatically learn to classify citations, and could provide a relevant alternative when compared with methods based on pattern extractions from the recent state of the art. The paper discusses their appropriateness given the requirement of large datasets to train neural networks.
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Contributor : Marc Bertin Connect in order to contact the contributor
Submitted on : Saturday, April 13, 2019 - 10:09:13 AM
Last modification on : Thursday, January 13, 2022 - 11:46:02 AM


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  • HAL Id : hal-02098831, version 1


Julien Perier-Camby, Marc Bertin, Iana Atanassova, Frédéric Armetta. A preliminary study to compare deep learning with rule-based approaches for citation classification. 8th International Workshop on Bibliometric-enhanced Information Retrieval (BIR) co-located with the 41st European Conference on Information Retrieval (ECIR 2019), Apr 2019, Cologne, Germany. pp.125-131. ⟨hal-02098831⟩



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