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Knowledge and Information Systems

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24.10.2020 | Regular Paper

Multiscale Laplacian graph kernel combined with lexico-syntactic patterns for biomedical event extraction from literature

verfasst von: Sabenabanu Abdulkadhar, Balu Bhasuran, Jeyakumar Natarajan

Erschienen in: Knowledge and Information Systems | Ausgabe 1/2021

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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.

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Titel: Multiscale Laplacian graph kernel combined with lexico-syntactic patterns for biomedical event extraction from literature
verfasst von: Sabenabanu Abdulkadhar
Balu Bhasuran
Jeyakumar Natarajan
Publikationsdatum: 24.10.2020
Verlag: Springer London
Erschienen in: Knowledge and Information Systems / Ausgabe 1/2021
Print ISSN: 0219-1377
Elektronische ISSN: 0219-3116
DOI: https://doi.org/10.1007/s10115-020-01514-8

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