Abstract
The exponential growth of research publications provides challenges for curators and researchers in finding and assimilating scientific facts described in the literature. Therefore, services that sup-port the browsing of articles and the identification of key concepts with minimal effort would be beneficial for the scientific community. Reference databases store such high value scientific facts and key concepts, in the form of annotations. Annotations are statements assigned by curators from an evidence in a publication. Yet, if annotated statements are linked with the publication’s references (e.g. PubMed identifiers), the evidences are rarely stored during the curation process. In this paper, we investigate the automatic relocalization of biological evidences, the Gene References Into Function (GeneRIFs), in scientific articles. GeneRIFs are free text statements extracted from an article, and potentially reformulated by a curator. De facto, only 33% of geneRIFs are copy-paste that can be retrieved by the reader with the search tool of his reader. For automatically retrieving the other evidences, we use an approximate string matching algorithm, based on a finite state automaton and a derivative Levenshtein distance. For evaluation, two hundred candidate sentences were evaluated by human experts. We present and compare results for the relocalization in both abstracts and fulltexts. With the optimal setting, 76% of the evidences are retrieved with a precision of 97%. This data free approach does not require any training data nor a priori lexical knowledge. Yet it remarkable how it handles with complex language modifications such as reformulations, acronyms expansion, or anaphora. In the whole MEDLINE, 350,000 geneRIFs were retrieved in abstracts, and 15,000 in fulltexts; they are currently available for highlighting in the Europe PMC literature browser.
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Acknowledgments
This research was supported by the Elixir Excelerate project, funded by the European Commission within the Research Infrastructures programme of Horizon 2020, grant agreement number 676559. The authors thank their colleagues from the SIB Swiss Institute of Bioinformatics (Core-IT), in particular Daniel Texeira and Heinz Stockinger, who provided insight and expertise that greatly assisted the research. The authors also thank the European Bioinformatics Institute, in particular Johanna McEntyre and Aravind Venkatesan, for the integration of retrieved geneRIFs into EuropePMC.
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Gobeill, J., Pasche, E., Ruch, P. (2023). Retrieving the Evidence of a Free Text Annotation in a Scientific Article: A Data Free Approach. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2019. Lecture Notes in Computer Science, vol 13451. Springer, Cham. https://doi.org/10.1007/978-3-031-24337-0_17
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