Today, full-texts of scientific articles are often stored in different locations than the used datasets. Dataset registries aim at a closer integration by making datasets citable but authors typically refer to datasets using inconsistent abbreviations and heterogeneous metadata (e.g. title, publication year). It is thus hard to reproduce research results, to access datasets for further analysis, and to determine the impact of a dataset. Manually detecting references to datasets in scientific articles is time-consuming and requires expert knowledge in the underlying research domain.We propose and evaluate a semi-automatic three-step approach for finding explicit references to datasets in social sciences articles.We first extract pre-defined special features from dataset titles in the da|ra registry, then detect references to datasets using the extracted features, and finally match the references found with corresponding dataset titles. The approach does not require a corpus of articles (avoiding the cold start problem) and performs well on a test corpus. We achieved an F-measure of 0.84 for detecting references in full-texts and an F-measure of 0.83 for finding correct matches of detected references in the da|ra dataset registry.
The problem that we aim to solve involves the two subtasks of 1. identifying dataset references in an article, and then 2. finding at least one correct match for each of these identified references. Literature citation mining is the process of determining the number of citations that a specific article receives. It constructs a literature citation network, which can be used for detecting the impact of an article @cite_11 . Citation mining can usually be handled by three subtasks. First, literature references should be extracted from the bibliography section of a document, and afterwards, metadata extraction should be applied on the references extracted in the first phase. Finally, each reference should be linked to the cited article by using the metadata extracted in the second step @cite_11 .
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Citations play an essential role in navigating academic literature and following chains of evidence in research. With the growing availability of large digital archives of scientific papers, the automated extraction and analysis of citations is becoming increasingly relevant. However, existing approaches to citation extraction still fall short of the high accuracy required to build more sophisticated and reliable tools for citation analysis and corpus navigation. In this paper, we present techniques for high accuracy extraction of citations and references from academic papers. By collecting multiple sources of evidence about entities from documents, and integrating citation extraction, reference segmentation, and citation-reference matching, we are able to significantly improve performance in subtasks including citation identification, author named entity recognition, and citation-reference matching. Applying our algorithm to previously-unseen documents, we demonstrate high F-measure performance of 0.980 for citation extraction, 0.983 for author named entity recognition, and 0.948 for citation-reference matching.