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We present the Pathway Curation (PC) task, a main event extraction task of the BioNLP shared task (ST) 2013. The PC task concerns the automatic extraction of biomolecular reactions from text. The task setting, representation and semantics are defined with respect to pathway model standards and ontolo-gies (SBML, BioPAX, SBO) and documents selected by(More)
Since their introduction in 2009, the BioNLP Shared Task events have been instrumental in advancing the development of methods and resources for the automatic extraction of information from the biomedical literature. In this paper, we present the Cancer Genetics (CG) and Pathway Curation (PC) tasks, two event extraction tasks introduced in the BioNLP Shared(More)
It has been known that a combination of multiple kernels and addition of various resources are the best options for improving effectiveness of kernel-based PPI extraction methods. These supplements , however, involve extensive kernel adaptation and feature selection processes, which attenuate the original benefits of the kernel methods. This paper shows(More)
While information retrieval (IR) and databases (DB) have been developed independently, there have been emerging requirements that both data management and efficient text retrieval should be supported simultaneously in an information system such as health care systems, bulletin boards, XML data management, and digital libraries. Recently DB-IR integration(More)
Text mining is a popular methodology for building Technology Intelligence which helps companies or organizations to make better decisions by providing knowledge about the state-of-the-art technologies obtained from the Internet or inside companies. As a matter of fact, the objects or events (so-called declarative knowledge) are the target knowledge that(More)
One of the key problems in upgrading information services towards knowledge services is to automatically mine latent topics, users' interests and their evolution patterns from large-scale S&T literatures. Most of current methods are devoted to either discover static latent topics and users' interests, or to analyze topic evolution only from intra-features(More)
Relation extraction refers to a method of efficiently detecting and identifying predefined semantic relationships within a set of entities in text documents. Numerous relation extractionfc techniques have been developed thus far, owing to their innate importance in the domain of information extraction and text mining. The majority of the relation extraction(More)