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Communication Dans Un Congrès Année : 2020

Automated Machine Learning for Information Retrieval in Scientific Articles

Résumé

The amount of scientific conferences and journal articles continues to increase and new approaches are required to support users in finding relevant publications. This study investigates to what extent a new machine learning (ML) pipeline may preferentially identify links between similar scientific articles. The characteristics of intersections and unions of keywords, contextualized keywords (i.e., synsets) and neighbors are computed and used to train a ML model. Automated machine learning (AutoML) is then applied to ease the search for a new pipeline. Extensive experiments demonstrated that a newly designed ML model achieves an accuracy of 90% on a dataset of approximately 120,000 article pairs. These results suggest that application of ML for proposing new recommendation systems could have in the long term a positive impact in the literature.
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Dates et versions

hal-03714819 , version 1 (12-09-2022)

Identifiants

Citer

Hojjat Rakhshani, Bastien Latard, Mathieu Brevilliers, Jonathan Weber, Julien Lepagnot, et al.. Automated Machine Learning for Information Retrieval in Scientific Articles. IEEE Congress on Evolutionary Computation (CEC), Jul 2020, Glasgow, United Kingdom. ⟨10.1109/CEC48606.2020.9185893⟩. ⟨hal-03714819⟩

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