Abstract
Molecular network structure to regulate plant seed development is very complex and to understand this from biomedical literature is a big challenge. Seed development is based on coordinated growth of different tissues, which are involved with complex genetics and environmental regulation. We develop a system for binary event extraction using statistical-, syntactic-, and dependency-based features. Experiments on the benchmark datasets of BioNLP-2016 SeeDev shared task show the recall, precision, and F-score values of 0.517, 0.399, and 0.451, respectively.
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Majumder, A., Ekbal, A., Naskar, S.K. (2020). Feature Selection Using NSGA-II for Event Extraction on Genetic and Molecular Mechanisms Involved in Plant Seed Development. In: Das, A., Nayak, J., Naik, B., Pati, S., Pelusi, D. (eds) Computational Intelligence in Pattern Recognition. Advances in Intelligent Systems and Computing, vol 999. Springer, Singapore. https://doi.org/10.1007/978-981-13-9042-5_4
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DOI: https://doi.org/10.1007/978-981-13-9042-5_4
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