A Natural Language Processing Pipeline for Detecting Informal Data References in Academic Literature
This work addresses the challenge for data librarians and researchers in social science by providing tools to automate the discovery of dataset references, though it is incremental as it builds on existing NLP methods.
The paper tackles the labor-intensive problem of linking publications to datasets by introducing an NLP pipeline that detects informal data references, increasing recall for literature review and enabling detection at scale.
Discovering authoritative links between publications and the datasets that they use can be a labor-intensive process. We introduce a natural language processing pipeline that retrieves and reviews publications for informal references to research datasets, which complements the work of data librarians. We first describe the components of the pipeline and then apply it to expand an authoritative bibliography linking thousands of social science studies to the data-related publications in which they are used. The pipeline increases recall for literature to review for inclusion in data-related collections of publications and makes it possible to detect informal data references at scale. We contribute (1) a novel Named Entity Recognition (NER) model that reliably detects informal data references and (2) a dataset connecting items from social science literature with datasets they reference. Together, these contributions enable future work on data reference, data citation networks, and data reuse.