Multiscale Laplacian graph kernel combined with lexico-syntactic patterns for biomedical event extraction from literatur
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Multiscale Laplacian graph kernel combined with lexico-syntactic patterns for biomedical event extraction from literature Sabenabanu Abdulkadhar1
· Balu Bhasuran2 · Jeyakumar Natarajan1,2
Received: 18 May 2019 / Revised: 22 September 2020 / Accepted: 27 September 2020 © Springer-Verlag London Ltd., part of Springer Nature 2020
Abstract 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. Keywords Bio-event extraction · Graph kernel · Multiscale Laplacian Graph kernel · Pattern matching rule engine · BioNLP-ST · GENIA-MK
Electronic supplementary material The online version of this article (https://doi.org/10.1007/s10115-020-0 1514-8) contains supplementary material, which is available to authorized users.
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Jeyakumar Natarajan [email protected]
1
Data Mining and Text Mining Laboratory, Department of Bioinformatics, Bharathiar University, Coimbatore, TamilNadu 641046, India
2
DRDO-BU Center for Life Sciences, Bharathiar University Campus, Coimbatore, TamilNadu 641046, India
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S. Abdulkadhar et al.
1 Introduction Advances in both biological and computational methods act as the catalyst for a large number of publications, especially in the biomedical domain [1]. Life science research outputs are widely disseminated as scientific articles, which can act as a source for knowledge discovery [2]. Recently biomedical text mining applications are developed using this literature with a focus on biological and clinical domain areas such as screening of clinical trials, pharmacogenetics, reaction detection and repurposing of drugs [3]. Initial efforts on text mining in the biomedical domain had a major focus on fundamental tasks like categorizing bio-entities (genes, proteins, diseases
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