Relation mining over a corpus of scientific literature

Authors:
Fabio Rinaldi;Gerold Schneider;Kaarel Kaljurand;Michael Hess;Christos Andronis;Andreas Persidis;Ourania Konstanti
Affiliations:
Institute of Computational Linguistics, IFI, University of Zurich, Switzerland;Institute of Computational Linguistics, IFI, University of Zurich, Switzerland;Institute of Computational Linguistics, IFI, University of Zurich, Switzerland;Institute of Computational Linguistics, IFI, University of Zurich, Switzerland;Biovista, Athens, Greece;Biovista, Athens, Greece;Biovista, Athens, Greece
Venue:
AIME'05 Proceedings of the 10th conference on Artificial Intelligence in Medicine
Year:
2005

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Abstract

The amount of new discoveries (as published in the scientific literature) in the area of Molecular Biology is currently growing at an exponential rate. This growth makes it very difficult to filter the most relevant results, and the extraction of the core information, for inclusion in one of the knowledge resources being maintained by the research community, becomes very expensive. Therefore, there is a growing interest in text processing approaches that can deliver selected information from scientific publications, which can limit the amount of human intervention normally needed to gather those results. This paper presents and evaluates an approach aimed at automating the process of extracting semantic relations (e.g. interactions between genes and proteins) from scientific literature in the domain of Molecular Biology. The approach, using a novel dependency-based parser, is based on a complete syntactic analysis of the corpus.