Comunicació presentada al WOSP 2018: 7th International Workshop on Mining Scientific Publications, celebrat el 7 de maig de 2018 a Miyazaki, Japó.

The identification of explicit and implicit citations to a given reference paper is important for numerous scientific text mining activities such as citation purpose identification, scientific opinion mining, and scientific summarization. This paper presents experiments on the identification of implicit citations in scientific papers. As in previous work, and relying on an annotated dataset of explicit and implicit citation sentences, we cast the problem as classification, evaluating several machine learning algorithms trained on a set of task-motivated features. We compare our work with the state of the art on the annotated dataset obtaining improved performance. We also annotate a new dataset which we make publicly available to validate our approach. The results on the new dataset confirm our set of features outperforms previously published research.

This work is (partly) supported by the Spanish Ministry of Economy and Competitiveness under the Maria de Maeztu Units of Excellence Programme (MDM-2015-0502) and by the TUNER project (TIN2015-65308-C5-5-R, MINECO/FEDER, UE).