Multiscale Laplacian graph kernel combined with lexico syntactic patterns for biomedical event extraction from literature
Bio event extraction is an extensive research area in the field of biomedical text mining, this focuses on elaborating relationships between biomolecules and can provide various aspects of their nature. Bio event extraction plays a vital role in biomedical literature mining applications such as biological network construction, pathway curation, and drug repurposing. Extracting biological events automatically is a difficult task because of the uncertainty and assortment of natural language processing such as negations and speculations, which provides further room for the development of feasible methodologies. This paper presents a hybrid approach that integrates an ensemble learning framework by combining a Multiscale Laplacian Graph kernel and a feature based linear kernel, using a pattern matching engine to identify biomedical events with arguments. This graph based kernel not only captures the topological relationships between the individual event nodes but also identifies the associations among the subgraphs for complex events. In addition, the lexico syntactic patterns were used to automatically discover the semantic role of each word in the sentence. For performance evaluation, we used the gold standard corpora, namely BioNLP ST (2009, 2011, and 2013) and GENIA MK. Experimental results show that our approach achieved better performance than other state of the art systems.
Knowl. Inf. Syst.