The second Maria de Maeztu Strategic Research Program (CEX2021-001195-M) of the Department of Information and Communication Technologies (DTIC) takes place between 2023 and 2026. The website for this program is under construction. You can find some details in this news.

The first María de Maeztu Strategic Research Program (MDM-2015-0502) took place between January 2016 and June 2020. It was focused on data-driven knowledge extraction, boosting synergistic research initiatives across our different research areas.

Back AbuRa’ed A, Chiruzzo L, Saggion H. Experiments in detection of implicit citations. OSP 2018: 7th International Workshop on Mining Scientific Publications

 

AbuRa’ed A, Chiruzzo L, Saggion H. Experiments in detection of implicit citations. OSP 2018: 7th International Workshop on Mining Scientific Publications

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

OA version at UPF e-repository: http://hdl.handle.net/10230/35180

Department of Information and Communication Technologies, UPF

Grant CEX2021-001195-M funded by MCIN/AEI /10.13039/501100011033


 


Department of Information and Communication Technologies, UPF

[email protected]

  • Àngel Lozano - Scientific director
  • Aurelio Ruiz - Program management