InTeReC: In-text Reference Corpus for Applying Natural Language Processing to Bibliometrics
Résumé
Bibliometrics is more and more interested in the full text processing and the study of the structure of scientific papers. The contexts of in-text references present in articles are particularly relevant for such studies. This work describes the construction of the InTeReC dataset, which is an in-text reference corpus that aims to promote experimental reproducibility and to provide a standard dataset for further research. The InTeReC dataset is a set of sentences containing in-text references together with all the data necessary for their recontextualization in papers using standard CSV format. This should encourage the implementation of natural language processing tools for Bibliometric studies and related research in information retrieval and visualization.