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. 2006 Nov 24;7 Suppl 3(Suppl 3):S3.
doi: 10.1186/1471-2105-7-S3-S3.

An environment for relation mining over richly annotated corpora: the case of GENIA

Fabio Rinaldi  1 , Gerold Schneider, Kaarel Kaljurand, Michael Hess, Martin Romacker
Affiliations

An environment for relation mining over richly annotated corpora: the case of GENIA

Fabio Rinaldi et al. BMC Bioinformatics. .

Abstract

Background: The biomedical domain is witnessing a rapid growth of the amount of published scientific results, which makes it increasingly difficult to filter the core information. There is a real need for support tools that 'digest' the published results and extract the most important information.

Results: We describe and evaluate an environment supporting the extraction of domain-specific relations, such as protein-protein interactions, from a richly-annotated corpus. We use full, deep-linguistic parsing and manually created, versatile patterns, expressing a large set of syntactic alternations, plus semantic ontology information.

Conclusion: The experiments show that our approach described is capable of delivering high-precision results, while maintaining sufficient levels of recall. The high level of abstraction of the rules used by the system, which are considerably more powerful and versatile than finite-state approaches, allows speedy interactive development and validation.

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Figures

Figure 1
Figure 1
Example of Dependency Tree. Tree of dependencies for a GENIA sentence, along with other linguistic annotations. Notice the additional deep-linguistic "control" subject dependency between token 7 and 4.
Figure 2
Figure 2
Sample Output. Sample output for the 'activate' relation.
See this image and copyright information in PMC

References

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